14.1.5.2 Multiple Classifiers, Combining Classifiers, Combinations

Chapter Contents (Back)
Classifier Combinations. Classifier Ensembles. Ensemble Classification. Data Fusion. 0009

Zaki, F.W., Abd el-Fattah, A.I., Enab, Y.M., el-Konyaly, S.H.,
An ensemble average classifier for pattern recognition machines,
PR(21), No. 4, 1988, pp. 327-332.
WWW Link. 0309
Less complex than Bayes. BibRef

Chen, C.C.[Chaur-Chin], Dubes, R.C.[Richard C.], Jain, A.K.[Anil K.],
Comments on 'An ensemble average classifier for pattern recognition machines',
PR(23), No. 6, 1990, pp. 669.
WWW Link. 0401
BibRef

Mandler, E., Schurmann, J.,
Combining the Classification Results of Independent Classifiers Based on the Dempster-Shafer Theory of Evidence,
PRAI-88(381-393). See also Mathematical Theory of Evidence, A. BibRef 8800

Lumelsky, V.J.[Vladimir J.],
A combined algorithm for weighting the variables and clustering in the clustering problem,
PR(15), No. 2, 1982, pp. 53-60.
WWW Link. 0309
BibRef

Mazurov, V.D., Krivonogov, A.I., Kazantsev, V.S.,
Solving of optimization and identification problems by the committee methods,
PR(20), No. 4, 1987, pp. 371-378.
WWW Link. 0309
BibRef

Lu, Y.[Yi], Yamaoka, F.[Fumiaki],
Fuzzy Integration of Classification Results,
PR(30), No. 11, November 1997, pp. 1877-1891.
WWW Link. 9801
Integrating classification results in a multiple classifier system using fuzzy reasoning. BibRef

Kuncheva, L.I.[Ludmila I.], Bezdek, J.C.[James C.], Duin, R.P.W.[Robert P.W.],
Decision templates for multiple classifier fusion: An Experimental Comparison,
PR(34), No. 2, February 2001, pp. 299-314.
WWW Link. 0011
BibRef

Windeatt, T.[Terry],
Vote counting measures for ensemble classifiers,
PR(36), No. 12, December 2003, pp. 2743-2756.
WWW Link. 0310
BibRef
And:
Diversity/accuracy and ensemble classifier design,
ICPR04(III: 454-457).
IEEE DOI 0409
BibRef

Windeatt, T., Ardeshir, G.,
Tree pruning for output coded ensembles,
ICPR02(II: 92-95).
IEEE DOI 0211
Convert a multi-class problem into several binary subproblems with an ensemble of binary classifiers. BibRef

Windeatt, T.[Terry], Zor, C.[Cemre],
Low Training Strength High Capacity Classifiers for Accurate Ensembles Using Walsh Coefficients,
SSSPR12(701-709).
Springer DOI 1211
BibRef

Smith, R.S.[Raymond Stuart], Windeatt, T.[Terry],
A Bias-Variance Analysis of Bootstrapped Class-Separability Weighting for Error-Correcting Output Code Ensembles,
ICPR10(61-64).
IEEE DOI 1008
BibRef

Windridge, D.[David], Kittler, J.V.[Josef V.],
A Morphologically Optimal Strategy for Classifier Combinaton: Multiple Expert Fusion as a Tomographic Process,
PAMI(25), No. 3, March 2003, pp. 343-353.
IEEE DOI 0301
Interpret combination as th eimplicit reconstruction of the composite probability density function. BibRef

Ji, C.Y., Ma, S.,
Combinations of Weak Classifiers,
TNN(8), No. 1, January 1997, pp. 32-42. 9701
BibRef

Woods, K.[Kevin], Kegelmeyer, W.P.[W. Philip], Bowyer, K.W.[Kevin W.],
Combination of Multiple Classifiers Using Local Accuracy Estimates,
PAMI(19), No. 4, April 1997, pp. 405-410.
IEEE DOI 9705
BibRef
Earlier: CVPR96(391-396).
IEEE DOI Combine classifiers using each individual classifier's accuracy in the region of feature space around the test sample. Includes pointers to the test data. BibRef

Kang, H.J., Kim, K., Kim, J.H.,
Optimal Approximation of Discrete Probability Distribution with Kth-Order Dependency and Its Application to Combining Multiple Classifiers,
PRL(18), No. 6, June 1997, pp. 515-523. 9710
BibRef

Bollacker, K.D., Ghosh, J.,
Knowledge Reuse in Multiple Classifier Systems,
PRL(18), No. 11-13, November 1997, pp. 1385-1390. 9806
BibRef

Fujino, A.[Akinori], Ueda, N.[Naonori], Saito, K.[Kazumi],
Semisupervised Learning for a Hybrid Generative/Discriminative Classifier based on the Maximum Entropy Principle,
PAMI(30), No. 3, March 2008, pp. 424-437.
IEEE DOI 0801
Classifier design. BibRef

Ueda, N.[Naonori],
Optimal Linear Combination of Neural Networks for Improving Classification Performance,
PAMI(22), No. 2, February 2000, pp. 207-215.
IEEE DOI 0003
Linearly combined. Minimum classifier error used to estimate weights. BibRef

Kleinberg, E.M.[Eugene M.],
On the Algorithmic Implementation of Stochastic Discrimination,
PAMI(22), No. 5, May 2000, pp. 473-490.
IEEE DOI 0008
Construct appropriate classifiers. Combine an arbitrary number of weak components BibRef

Saranli, A.[Afar], Demirekler, M.[Mübeccel],
A statistical unified framework for rank-based multiple classifier decision combination,
PR(34), No. 4, April 2001, pp. 865-884.
WWW Link. 0101
BibRef
Earlier:
A Unified View of Rank-based Decision Combination,
ICPR00(Vol II: 479-482).
IEEE DOI 0009
BibRef

Saranli, A.[Afar], Demirekler, M.[Mübeccel],
On output independence and complementariness in rank-based multiple classifier decision systems,
PR(34), No. 12, December 2001, pp. 2319-2330.
WWW Link. 0110
It is shown that output independence of classifiers is not a requirement for achieving complementariness between these classifiers. BibRef

Altinçay, H.[Hakan], Demirekler, M.[Mübeccel],
Undesirable effects of output normalization in multiple classifier systems,
PRL(24), No. 9-10, June 2003, pp. 1163-1170.
WWW Link. 0304
BibRef
Earlier:
Why does output normalization create problems in multiple classifier systems?,
ICPR02(II: 775-778).
IEEE DOI 0211
BibRef

Sasikala, K.R., Petrou, M.,
Properties of the generalised fuzzy aggregation operators,
PRL(22), No. 1, January 2001, pp. 15-24.
Elsevier DOI 0105
BibRef
Earlier: A2, A1:
On the Relationship between Neural Networks and Fuzzy Reasoning,
ICPR96(IV: 239-243).
IEEE DOI 9608
(Univ. of Surrey, UK) BibRef

Parikh, C.R.[Chinmay R.], Pont, M.J.[Michael J.], Jones, N.B.[N. Barrie],
Application of Dempster-Shafer Theory in Condition Monitoring Applications: A Case Study,
PRL(22), No. 6-7, May 2001, pp. 777-785.
Elsevier DOI 0105
See also Mathematical Theory of Evidence, A. BibRef

Luo, Y., Chambers, J.A., Lambotharan, S.,
Global convergence and mixing parameter selection in the cross-correlation constant modulus algorithm for the multi-user environment,
VISP(148), No. 1, February 2001, pp. 9-20. 0105
BibRef

Valev, V.[Ventzeslav], Asaithambi, A.[Asai],
Multidimensional pattern recognition problems and combining classifiers,
PRL(22), No. 12, October 2001, pp. 1291-1297.
Elsevier DOI 0108
BibRef

Valev, V.[Ventzeslav],
Supervised pattern recognition by parallel feature partitioning,
PR(37), No. 3, March 2004, pp. 463-467.
WWW Link. 0401
Partition the feature space to a minimal number of nonintersecting regions. Achieved by solving an integer-valued optimization problem. BibRef

Valev, V.[Ventzeslav], Yanev, N.[Nicola],
Classification using graph partitioning,
ICPR12(1261-1264).
WWW Link. 1302
BibRef

Valev, V.[Ventzeslav],
From binary features to Non-Reducible Descriptors in supervised pattern recognition problems,
PRL(45), No. 1, 2014, pp. 106-114.
Elsevier DOI 1407
Machine learning of Boolean formulas BibRef

Kupinski, M.A., Anastasio, M.A.,
Multiobjective genetic optimization of diagnostic classifiers with implications for generating receiver operating characteristic curves,
MedImg(18), No. 8, August 1999, pp. 675-685.
IEEE Top Reference. 0110
BibRef

Demrekler, M.[Mübeccel], Altnçay, H.[Hakan],
Plurality Voting-Based Multiple Classifier Systems: Statistically Independent with Respect to Dependent Classifier Sets,
PR(35), No. 11, November 2002, pp. 2365-2379.
WWW Link. 0208
BibRef

Altnçay, H.[Hakan],
On naive Bayesian fusion of dependent classifiers,
PRL(26), No. 15, November 2005, pp. 2463-2473.
WWW Link. 0510
BibRef

Bovino, L., Dimauro, G., Impedovo, S., Lucchese, M.G., Modugno, R., Pirlo, G., Salzo, A., Sarcinella, L.,
On the combination of abstract-level classifiers,
IJDAR(6), No. 1, 2003, pp. 42-54.
Springer DOI 0308
BibRef

Bovino, L., Dimauro, G., Impedovo, S., Pirlo, G., Salzo, A.,
Increasing the Number of Classifiers in Multi-classifier Systems: A Complementarity-Based Analysis,
DAS02(145 ff.).
Springer DOI 0303
BibRef

Impedovo, D.[Donato], Pirlo, G.[Giuseppe],
Generating Sets of Classifiers for the Evaluation of Multi-expert Systems,
ICPR10(2166-2169).
IEEE DOI 1008
BibRef

Dimauro, G., Impedovo, S., Lucchese, M.G., Pirlo, G., Salzo, A.,
Discovering Rules for Dynamic Configuration of Multi-classifier Systems,
DAS02(157 ff.).
Springer DOI 0303
BibRef

di Lecce, V., Dimauro, G., Guerriero, A., Impedovo, S., Pirlo, G., Salzo, A.,
Knowledge-based methods for classifier combination: An experimental investigation,
CIAP99(562-565).
IEEE DOI 9909
BibRef

Pirlo, G.[Giuseppe], Impedovo, D.[Donato],
Adaptive Score Normalization for Output Integration in Multiclassifier Systems,
SPLetters(19), No. 12, December 2012, pp. 837-840.
IEEE DOI 1212
BibRef

Pirlo, G.[Giuseppe], Impedovo, D.[Donato], Trullo, C.A.[Claudia Adamita], Stasolla, E.[Erasmo],
Combination of Measurement-Level Classifiers: Output Normalization by Dynamic Time Warping,
ICDAR09(416-420).
IEEE DOI 0907
BibRef

Pirlo, G.[Giuseppe], Trullo, C.A.[Claudia Adamita], Impedovo, D.[Donato],
A Feedback-Based Multi-Classifier System,
ICDAR09(713-717).
IEEE DOI 0907
BibRef

Impedovo, D., Pirlo, G., Sarcinella, L., Stasolla, E.,
Artificial Classifier Generation for Multi-expert System Evaluation,
FHR10(421-426).
IEEE DOI 1011
BibRef

Raudys, S.J.[Sarunas J.],
Experts' boasting in trainable fusion rules,
PAMI(25), No. 9, September 2003, pp. 1178-1182.
IEEE Abstract. 0309
Experts can lead to biases in fusion rules if training of experts and fusion rules use the same data. BibRef

Bachmann, C.M., Bettenhausen, M.H., Fusina, R.A., Donato, T.F., Russ, A.L., Burke, J.W., Lamela, G.M., Rhea, W.J., Truitt, B.R., Porter, J.H.,
A credit assignment approach to fusing classifiers of multiseason hyperspectral imagery,
GeoRS(41), No. 11, November 2003, pp. 2488-2499.
IEEE Abstract. 0311
BibRef

Rohlfing, T., Russakoff, D.B., Maurer, Jr., C.R.,
Performance-Based Classifier Combination in Atlas-Based Image Segmentation Using Expectation-Maximization Parameter Estimation,
MedImg(23), No. 8, August 2004, pp. 983-994.
IEEE Abstract. 0409
Estimate performance of individual classifiers and combine. BibRef

Rohlfing, T.[Torsten], Maurer, Jr., C.R.[Calvin R.],
Multi-classifier framework for atlas-based image segmentation,
PRL(26), No. 13, 1 October 2005, pp. 2070-2079.
WWW Link. 0509
BibRef
Earlier: CVPR04(I: 255-260).
IEEE DOI 0408
BibRef

Melnik, O.[Ofer], Vardi, Y.[Yehuda], Zhang, C.H.[Cun-Hui],
Mixed Group Ranks: Preference and Confidence in Classifier Combination,
PAMI(26), No. 8, August 2004, pp. 973-981.
IEEE Abstract. 0407
Analyze rules for combining when a large number of classes (biometrics). BibRef

Singh, S.[Sameer], Singh, M.[Maneesha],
A dynamic classifier selection and combination approach to image region labelling,
SP:IC(20), No. 3, March 2005, pp. 219-231.
WWW Link. 0501
BibRef

Zhu, H.[Hui], Tang, X.L.[Xiang-Long],
Classifier geometrical characteristic comparison and its application in classifier selection,
PRL(26), No. 6, 1 May 2005, pp. 829-842.
WWW Link. 0501
BibRef

Neumann, J.[Julia], Schnörr, C.[Christoph], Steidl, G.[Gabriele],
Efficient wavelet adaptation for hybrid wavelet-large margin classifiers,
PR(38), No. 11, November 2005, pp. 1815-1830.
WWW Link. 0509
BibRef
Earlier:
Feasible Adaptation Criteria for Hybrid Wavelet: Large Margin Classifiers,
CAIP03(588-595).
Springer DOI 0311
BibRef

Zouari, H.[Héla], Heutte, L.[Laurent], Lecourtier, Y.[Yves],
Controlling the diversity in classifier ensembles through a measure of agreement,
PR(38), No. 11, November 2005, pp. 2195-2199.
WWW Link. 0509
BibRef
And:
Experimental Comparison of Combination Rules using Simulated Data,
ICPR06(III: 152-155).
IEEE DOI 0609
BibRef

Zouari, H., Heutte, L., Lecourtier, Y., Alimi, A.,
A new classifier simulator for evaluating parallel combination methods,
ICDAR03(26-30).
IEEE DOI 0311
BibRef

Demir, C.[Cigdem], Alpaydin, E.[Ethem],
Cost-conscious classifier ensembles,
PRL(26), No. 14, 15 October 2005, pp. 2206-2214.
WWW Link. 0510
BibRef

Alpaydin, E.[Ethem],
Multiple neural networks and weighted voting,
ICPR92(II:29-32).
IEEE DOI 9208
BibRef

Topchy, A.P., Jain, A.K., Punch, W.F.,
Clustering Ensembles: Models of Consensus and Weak Partitions,
PAMI(27), No. 12, December 2005, pp. 1866-1881.
IEEE DOI 0512
First uniform representation for multiple classifiers. Probabilistic model of consensus. BibRef

Topchy, A.P., Minaei-Bidgoli, B., Jain, A.K., Punch, W.F.,
Adaptive clustering ensembles,
ICPR04(I: 272-275).
IEEE DOI 0409
BibRef

Parvin, H.[Hamid], Parvin, S.[Sajad], Rezaei, Z.[Zahra], Mohamadi, M.[Moslem],
A Heuristically Perturbation of Dataset to Achieve a Diverse Ensemble of Classifiers,
MCPR12(197-206).
Springer DOI 1208
BibRef

Parvin, H.[Hamid], Parvin, S.[Sajad],
Unsupervised Linkage Learner Based on Local Optimums,
MCPR12(255-264).
Springer DOI 1208
BibRef

Alizadeh, H.[Hosein], Minaei-Bidgoli, B.[Behrouz], Parvin, H.[Hamid],
A New Asymmetric Criterion for Cluster Validation,
CIARP11(320-330).
Springer DOI 1111
BibRef

Aksela, M.[Matti], Laaksonen, J.T.[Jorma T.],
Using diversity of errors for selecting members of a committee classifier,
PR(39), No. 4, April 2006, pp. 608-623.
WWW Link. Classifier combining; Committee classifier; Diversity; Diversity of errors 0604
BibRef

Aksela, M., Girdziusas, R., Laaksonen, J.T., Oja, E., Kangas, J.,
Class-confidence critic combining,
FHR02(201-206).
IEEE Top Reference. 0209
BibRef

Garcia-Pedrajas, N.[Nicolas], Ortiz-Boyer, D.,
Improving Multiclass Pattern Recognition by the Combination of Two Strategies,
PAMI(28), No. 6, June 2006, pp. 1001-1006.
IEEE DOI 0605
BibRef

García-Pedrajas, N.[Nicolás], Ortiz-Boyer, D.[Domingo],
A cooperative constructive method for neural networks for pattern recognition,
PR(40), No. 1, January 2007, pp. 80-98.
WWW Link. 0611
Constructive algorithms; Pattern classification; Evolutionary computation; Neural networks; Cooperative coevolution BibRef

Garcia-Pedrajas, N.[Nicolas],
Supervised projection approach for boosting classifiers,
PR(42), No. 9, September 2009, pp. 1742-1760.
Elsevier DOI 0905
Classification; Ensembles of classifiers; Boosting; Supervised projections BibRef

Lu, Z.W.[Zhi-Wu],
A regularized minimum cross-entropy algorithm on mixtures of experts for time series prediction and curve detection,
PRL(27), No. 9, July 2006, pp. 947-955.
WWW Link. Regularization theory; Model selection; Time series prediction; Curve detection 0605
BibRef

Goodband, J.H., Haas, O.C.L., Mills, J.A.,
A mixture of experts committee machine to design compensators for intensity modulated radiation therapy,
PR(39), No. 9, September 2006, pp. 1704-1714.
WWW Link. 0606
Committee machines; Neural networks; Fuzzy C-means; Compensators; Radiation therapy BibRef

Wanas, N.M.[Nayer M.], Dara, R.A.[Rozita A.], Kamel, M.S.[Mohamed S.],
Adaptive fusion and co-operative training for classifier ensembles,
PR(39), No. 9, September 2006, pp. 1781-1794.
WWW Link. 0606
Decision fusion; Co-operative training; Combining architecture BibRef

Dara, R.A.[Rozita A.], Kamel, M.S.[Mohamed S.], Wanas, N.M.[Nayer M.],
Data dependency in multiple classifier systems,
PR(42), No. 7, July 2009, pp. 1260-1273.
Elsevier DOI 0903
Multiple classifier systems; Data dependency; Aggregation methods; Stability; Class imbalance BibRef

Chen, L.[Lei], Kamel, M.S.[Mohamed S.],
A generalized adaptive ensemble generation and aggregation approach for multiple classifier systems,
PR(42), No. 5, May 2009, pp. 629-644.
Elsevier DOI 0902
Pattern recognition; Data mining; Classification; Classifier combination; Multiple classifier systems BibRef

Hu, T.M.[Tian-Ming], Yu, Y.[Ying], Xiong, J.Z.[Jin-Zhi], Sung, S.Y.[Sam Yuan],
Maximum likelihood combination of multiple clusterings,
PRL(27), No. 13, 1 October 2006, pp. 1457-1464.
WWW Link. 0606
Consensus clustering; Centroid clustering; Markov random field; Metric distance function BibRef

Rooney, N.[Niall], Patterson, D.[David],
A weighted combination of stacking and dynamic integration,
PR(40), No. 4, April 2007, pp. 1385-1388.
WWW Link. 0701
Ensemble learning; Stacking; Regression BibRef

Rooney, N.[Niall], Patterson, D.[David], Nugent, C.[Chris],
Non-strict heterogeneous Stacking,
PRL(28), No. 9, 1 July 2007, pp. 1050-1061.
WWW Link. 0704
Ensemble learning; Meta learning; Regression BibRef

Hu, Q.H.[Qing-Hua], Yu, D.R.[Da-Ren], Xie, Z.X.[Zong-Xia], Li, X.D.[Xiao-Dong],
EROS: Ensemble rough subspaces,
PR(40), No. 12, December 2007, pp. 3728-3739.
WWW Link. 0709
Ensemble systems initially improve performance as more added, the decrease. Attribute reduction; Ensemble learning; Multiple classifier system; Rough set; Selective ensemble BibRef

Jarillo, G.[Gabriel], Pedrycz, W.[Witold], Reformat, M.[Marek],
Aggregation of classifiers based on image transformations in biometric face recognition,
MVA(19), No. 2, March 2008, pp. 125-140.
Springer DOI 0802
BibRef

Kim, Y.W.[Young-Won], Oh, I.S.[Il-Seok],
Classifier ensemble selection using hybrid genetic algorithms,
PRL(29), No. 6, 15 April 2008, pp. 796-802.
WWW Link. 0803
Multiple classifier combination; Ensemble selection; Genetic algorithm; Local search operation See also Local search-embedded genetic algorithms for feature selection. BibRef

Bogdanov, A.V.,
Neuroinspired Architecture for Robust Classifier Fusion of Multisensor Imagery,
GeoRS(46), No. 5, May 2008, pp. 1467-1487.
IEEE DOI 0804
BibRef

Asdornwised, W.[Widhyakorn], Jitapunkul, S.[Somchai],
Multiple Description Pattern Analysis: Robustness to Misclassification Using Local Discriminant Frame Expansions,
IEICE(E88-D), No. 10, October 2005, pp. 2296-2307.
DOI Link 0510
Apply to ATR task. BibRef

Toh, K.A.[Kar-Ann], Kim, J.H.[Jai-Hie], Lee, S.Y.[Sang-Youn],
Maximizing area under ROC curve for biometric scores fusion,
PR(41), No. 11, November 2008, pp. 3373-3392.
WWW Link. 0808
Receiver operating characteristics; Biometrics; Decision fusion; Machine learning; Pattern classification BibRef

Tumer, K.[Kagan], Agogino, A.K.[Adrian K.],
Ensemble clustering with voting active clusters,
PRL(29), No. 14, October 2008, pp. 1947-1953.
WWW Link. 0804
Cluster ensembles; Consensus clustering; Distributed clustering; Adaptive clustering BibRef

Carneiro, G.[Gustavo], Vasconcelos, N.M.[Nuno M.],
Minimum Bayes error features for visual recognition,
IVC(27), No. 1-2, January 2009, pp. 131-140.
WWW Link. 0811
BibRef
Earlier:
Minimum Bayes Error Features for Visual Recognition by Sequential Feature Selection and Extraction,
CRV05(253-260).
IEEE DOI 0505
Visual recognition; Feature selection; Feature extraction; Minimum Bayes error; Mixture models; Face recognition; Texture recognition; Object recognition BibRef

Martínez-Muñoz, G.[Gonzalo], Hernández-Lobato, D.[Daniel], Suárez, A.[Alberto],
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation,
PAMI(31), No. 2, February 2009, pp. 245-259.
IEEE DOI 0901
reduce size of ensembles for classifiers. BibRef

Hernández-Lobato, D.[Daniel], Martínez-Muñoz, G.[Gonzalo], Suárez, A.[Alberto],
Statistical Instance-Based Pruning in Ensembles of Independent Classifiers,
PAMI(31), No. 2, February 2009, pp. 364-369.
IEEE DOI 0901
BibRef

Bacauskiene, M.[Marija], Verikas, A.[Antanas], Gelzinis, A.[Adas], Valincius, D.,
A feature selection technique for generation of classification committees and its application to categorization of laryngeal images,
PR(42), No. 5, May 2009, pp. 645-654.
Elsevier DOI 0902
Feature selection; Variable selection; Classification committee; Genetic search; Support vector machine; Laryngeal image BibRef

Wang, X.[Xi], Yang, C.[Chunyu], Zhou, J.[Jie],
Clustering aggregation by probability accumulation,
PR(42), No. 5, May 2009, pp. 668-675.
Elsevier DOI 0902
BibRef
Earlier:
Spectral aggregation for clustering ensemble,
ICPR08(1-4).
IEEE DOI 0812
Clustering aggregation; Evidence accumulation; Probability accumulation BibRef

Chaudhuri, P.[Probal], Ghosh, A.K.[Anil K.], Oja, H.[Hannu],
Classification Based on Hybridization of Parametric and Nonparametric Classifiers,
PAMI(31), No. 7, July 2009, pp. 1153-1164.
IEEE DOI 0905
Deal with problems where parametric classifier assumptions fail, but a small number of training samples cause nonparameteric failures. Combine strengths of each. BibRef

Terrades, O.R.[Oriol Ramos], Valveny, E.[Ernest], Tabbone, S.A.[Salvatore A.],
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework,
PAMI(31), No. 9, September 2009, pp. 1630-1644.
IEEE DOI 0907
BibRef

Couturier, S.[Stéphane], Mas, J.F.[Jean-François], Mountrakis, G.[Giorgos], Watts, R.[Raymond], Luo, L.[Lori], Wang, J.[Jida],
Developing Collaborative Classifiers using an Expert-based Model,
PhEngRS(75), No. 7, July 2009, pp. 831-844.
WWW Link. 0910
A novel framework for integrating classifiers of variable complexity as applied to specific portions of the input space. BibRef

Correa-Morris, J.[Jyrko], Espinosa-Isidron, D.L.[Dustin L.], Alvarez-Nadiozhin, D.R.[Denis R.],
An incremental nested partition method for data clustering,
PR(43), No. 7, July 2010, pp. 2439-2455.
Elsevier DOI 1003
Nested partition; Data clustering; Incremental clustering BibRef

Correa-Morris, J.[Jyrko], Ruiz-Shulcloper, J.[Jose], Espinosa-Isidron, D.L.[Dustin L.], Pons-Porrata, A.[Aurora],
Incremental nested partition method,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Vega-Pons, S.[Sandro], Correa-Morris, J.[Jyrko], Ruiz-Shulcloper, J.[José],
Weighted partition consensus via kernels,
PR(43), No. 8, August 2010, pp. 2712-2724.
Elsevier DOI 1006
BibRef
Earlier:
Weighted Cluster Ensemble Using a Kernel Consensus Function,
CIARP08(195-202).
Springer DOI 0809
Cluster ensemble; Kernel function; Similarity measure; Clustering validity index; Consensus partition BibRef

Correa-Morris, J.[Jyrko],
An indication of unification for different clustering approaches,
PR(46), No. 9, September 2013, pp. 2548-2561.
Elsevier DOI 1305
Data clustering; Clustering function; Impossibility theorem BibRef

Correa-Morris, J.[Jyrko], Hernández, N.[Noslen],
On the Comparison of Structured Data,
CIARP12(479-486).
Springer DOI 1209
BibRef

Vega-Pons, S.[Sandro], Ruiz-Shulcloper, J.[José], Guerra-Gandón, A.[Alejandro],
Weighted association based methods for the combination of heterogeneous partitions,
PRL(32), No. 16, 1 December 2011, pp. 2163-2170.
Elsevier DOI 1112
Clustering ensemble; Similarity measure; Evidence accumulation; Object representation; Kernel function BibRef

Vega-Pons, S.[Sandro], Ruiz-Shulcloper, J.[José],
Clustering Ensemble Method for Heterogeneous Partitions,
CIARP09(481-488).
Springer DOI 0911
BibRef

Khreich, W.[Wael], Granger, E.[Eric], Miri, A.[Ali], Sabourin, R.[Robert],
Iterative Boolean combination of classifiers in the ROC space: An application to anomaly detection with HMMs,
PR(43), No. 8, August 2010, pp. 2732-2752.
Elsevier DOI 1006
BibRef
And:
Boolean Combination of Classifiers in the ROC Space,
ICPR10(4299-4303).
IEEE DOI 1008
Receiver operating characteristics; Combination of classifiers; Limited and imbalanced data; Hidden Markov models; Anomaly detection; Computer and network security BibRef

Khreich, W.[Wael], Granger, E.[Eric], Miri, A.[Ali], Sabourin, R.[Robert],
Adaptive ROC-based ensembles of HMMs applied to anomaly detection,
PR(45), No. 1, January 2012, pp. 208-230.
Elsevier DOI 1109
Classification; Multi-classifier systems; Incremental learning; Adaptive systems; ROC; Information fusion; Hidden Markov models; Anomaly detection; Computer and network security BibRef

Khreich, W.[Wael], Granger, E.[Eric], Miri, A.[Ali], Sabourin, R.[Robert],
On the memory complexity of the forward-backward algorithm,
PRL(31), No. 2, 15 January 2010, pp. 91-99.
Elsevier DOI 1001
Hidden Markov Models; Forward-backward; Baum-Welch; Forward Filtering Backward Smoothing; Complexity analysis Comment: See also Comment on the paper 'On the memory complexity of the forward-backward algorithm,'. BibRef

Connolly, J.F.[Jean-François], Granger, E.[Eric], Sabourin, R.[Robert],
Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition,
PR(45), No. 7, July 2012, pp. 2460-2477.
Elsevier DOI 1203
Classification; Heterogeneous ensembles; Diversity; Incremental learning; Adaptive multiclassifier systems; ARTMAP neural networks; Biometrics; Face recognition in video BibRef

Connolly, J.F.[Jean-Francois], Granger, E.[Eric], Sabourin, R.[Robert],
On the correlation between genotype and classifier diversity,
ICPR12(1068-1071).
WWW Link. 1302
BibRef

Meynet, J.[Julien], Thiran, J.P.[Jean-Philippe],
Information theoretic combination of pattern classifiers,
PR(43), No. 10, October 2010, pp. 3412-3421.
Elsevier DOI 1007
Machine learning; Pattern recognition; Classifier combination; Information theory; Mutual information; Diversity BibRef

Zhang, L.[Li], Zhou, W.D.[Wei-Da],
Sparse ensembles using weighted combination methods based on linear programming,
PR(44), No. 1, January 2011, pp. 97-106.
Elsevier DOI 1003
Classifier ensemble; Linear weighted combination; Linear programming; Sparse ensembles; k nearest neighbor BibRef

Montalvao, J.[Jugurta], Canuto, J.[Janio],
Clustering ensembles and space discretization: A new regard toward diversity and consensus,
PRL(31), No. 15, 1 November 2010, pp. 2415-2424.
Elsevier DOI 1003
Clustering ensembles; Weak partitions; ANMI criterion; Binary morphology BibRef

Moshtaghi, M.[Masud], Rajasegarar, S.[Sutharshan], Leckie, C.[Christopher], Karunasekera, S.[Shanika],
An efficient hyperellipsoidal clustering algorithm for resource-constrained environments,
PR(44), No. 9, September 2011, pp. 2197-2209.
Elsevier DOI 1106
HyCARCE; Data clustering; Hyperellipsoidal clustering; Wireless sensor networks; Low computational cost clustering algorithm BibRef

Rajasegarar, S.[Sutharshan], Gluhak, A.[Alexander], Imran, M.A.[Muhammad Ali], Nati, M.[Michele], Moshtaghi, M.[Masud], Leckie, C.[Christopher], Palaniswami, M.[Marimuthu],
Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks,
PR(47), No. 9, 2014, pp. 2867-2879.
Elsevier DOI 1406
Anomaly detection BibRef

Rokach, L.[Lior],
Pattern Classification Using Ensemble Methods,
World ScientificSingapore, 2009 ISBN: 978-981-4271-06-6
HTML Version. Survay, Pattern Classification. Buy this book: Pattern Classification Using Ensemble Methods (Series in Machine Perception and Artificial Intelligence) Describe classical methods and new approaches to help determine which to use for particular problems. BibRef 0900

Mimaroglu, S.[Selim], Erdil, E.[Ertunc],
Combining multiple clusterings using similarity graph,
PR(44), No. 3, March 2011, pp. 694-703.
Elsevier DOI 1011
Clustering; Combining clustering partitions; Cluster ensemble; Evidence accumulation; Robust clustering; Mutual information BibRef

Mimaroglu, S.[Selim], Aksehirli, E.[Emin],
Improving DBSCAN's execution time by using a pruning technique on bit vectors,
PRL(32), No. 13, 1 October 2011, pp. 1572-1580.
Elsevier DOI 1109
Clustering; DBSCAN; Binary methods; Pruning BibRef

Christou, I.T.[Ioannis T.],
Coordination of Cluster Ensembles via Exact Methods,
PAMI(33), No. 2, February 2011, pp. 279-293.
IEEE DOI 1101
Optimization cluster combinations. BibRef

Guru, D.S., Suraj, M.G., Manjunath, S.,
Fusion of covariance matrices of PCA and FLD,
PRL(32), No. 3, 1 February 2011, pp. 432-440.
Elsevier DOI 1101
Classifier fusion; Appearance based approach; Covariance matrix; Data clustering; Video retrieval See also Archival and retrieval of symbolic images: An invariant scheme based on triangular spatial relationship. BibRef

Gurrutxaga, I.[Ibai], Muguerza, J.[Javier], Arbelaitz, O.[Olatz], Perez, J.M.[Jesus M.], Martin, J.I.[Jose I.],
Towards a standard methodology to evaluate internal cluster validity indices,
PRL(32), No. 3, 1 February 2011, pp. 505-515.
Elsevier DOI 1101
Cluster validation; Cluster validity index; Unsupervised learning BibRef

Yera, A.[Ainhoa], Arbelaitz, O.[Olatz], Jodra, J.L.[José L.], Gurrutxaga, I.[Ibai], Pérez, J.M.[Jesús M.], Muguerza, J.[Javier],
Analysis of several decision fusion strategies for clustering validation. Strategy definition, experiments and validation,
PRL(85), No. 1, 2017, pp. 42-48.
Elsevier DOI 1612
Cluster validity index BibRef

Arbelaitz, O.[Olatz], Gurrutxaga, I.[Ibai], Muguerza, J.[Javier], Pérez, J.M.[Jesús M.], Perona, I.[Iñigo],
An extensive comparative study of cluster validity indices,
PR(46), No. 1, January 2013, pp. 243-256.
Elsevier DOI 1209
Award, Pattern Recognition, Honorable Mention. Crisp clustering; Cluster validity index; Comparative analysis See also SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index. BibRef

Mao, S.[Shasha], Jiao, L.C., Xiong, L.[Lin], Gou, S.P.[Shui-Ping],
Greedy optimization classifiers ensemble based on diversity,
PR(44), No. 6, June 2011, pp. 1245-1261.
Elsevier DOI 1102
Diversity; Matching pursuit; Greedy optimization; Residual; Selective ensemble; Kappa-error diagram BibRef

Hernandez-Lobato, D.[Daniel], Martinez-Munoz, G.[Gonzalo], Suarez, A.[Alberto],
Inference on the prediction of ensembles of infinite size,
PR(44), No. 7, July 2011, pp. 1426-1434.
Elsevier DOI 1103
Classification ensembles; Classification trees; Bayesian inference; Infinite ensembles BibRef

Irle, A.[Albrecht], Kauschke, J.[Jonas],
On Kleinberg's Stochastic Discrimination Procedure,
PAMI(33), No. 7, July 2011, pp. 1482-1486.
IEEE DOI 1106
See also On the Algorithmic Implementation of Stochastic Discrimination. BibRef

Ricamato, M.T.[Maria Teresa], Tortorella, F.[Francesco],
Partial AUC maximization in a linear combination of dichotomizers,
PR(44), No. 10-11, October-November 2011, pp. 2669-2677.
Elsevier DOI 1101
BibRef
Earlier:
AUC-based Combination of Dichotomizers: Is Whole Maximization also Effective for Partial Maximization?,
ICPR10(73-76).
IEEE DOI 1008
Combination of classifiers; ROC analysis; Partial AUC BibRef

Asman, A.J., Landman, B.A.,
Robust Statistical Label Fusion Through Consensus Level, Labeler Accuracy, and Truth Estimation (COLLATE),
MedImg(30), No. 10, October 2011, pp. 1779-1794.
IEEE DOI 1110
BibRef

Iam-On, N.[Natthakan], Boongoen, T.[Tossapon], Garrett, S.[Simon], Price, C.[Chris],
A Link-Based Approach to the Cluster Ensemble Problem,
PAMI(33), No. 12, December 2011, pp. 2396-2409.
IEEE DOI 1110
Also use similarity between clusters in consensus. BibRef

Lu, Z.W.[Zhi-Wu], Peng, Y.X.[Yu-Xin], Ip, H.H.S.[Horace H.S.],
Combining multiple clusterings using fast simulated annealing,
PRL(32), No. 15, 1 November 2011, pp. 1956-1961.
Elsevier DOI 1112
Clustering ensemble; Comparing clusterings; Simulated annealing BibRef

Tian, J.[Jin], Li, M.Q.[Min-Qiang], Chen, F.[Fuzan], Kou, J.[Jisong],
Coevolutionary learning of neural network ensemble for complex classification tasks,
PR(45), No. 4, April 2012, pp. 1373-1385.
Elsevier DOI 1112
Ensemble learning; Neural network; Coevolutionary algorithm; Classification BibRef

Meo, R.[Rosa], Bachar, D.[Dipankar], Ienco, D.[Dino],
LODE: A distance-based classifier built on ensembles of positive and negative observations,
PR(45), No. 4, April 2012, pp. 1409-1425.
Elsevier DOI 1112
Data mining; Supervised learning; Concept learning; Associative classifier BibRef

Zhang, S.H.[Shao-Hong], Wong, H.S.[Hau-San], Shen, Y.[Ying],
Generalized Adjusted Rand Indices for cluster ensembles,
PR(45), No. 6, June 2012, pp. 2214-2226.
Elsevier DOI 1202
BibRef
Earlier: A1, A2, Only:
ARImp: A Generalized Adjusted Rand Index for Cluster Ensembles,
ICPR10(778-781).
IEEE DOI 1008
Cluster ensembles; Clustering evaluation; Adjusted Rand Index BibRef

Cavalin, P.R.[Paulo R.], Sabourin, R.[Robert], Suen, C.Y.[Ching Y.],
LoGID: An adaptive framework combining local and global incremental learning for dynamic selection of ensembles of HMMs,
PR(45), No. 9, September 2012, pp. 3544-3556.
Elsevier DOI 1206
Adaptive systems; Ensembles of classifiers; Incremental learning; Dynamic selection; Hidden Markov models BibRef

Cruz, R.M.O.[Rafael M.O.], Sabourin, R.[Robert], Cavalcanti, G.D.C.[George D.C.], Ren, T.I.[Tsang Ing],
META-DES: A dynamic ensemble selection framework using meta-learning,
PR(48), No. 5, 2015, pp. 1925-1935.
Elsevier DOI 1502
BibRef
Earlier: A1, A2, A3, Only:
On Meta-learning for Dynamic Ensemble Selection,
ICPR14(1230-1235)
IEEE DOI 1412
Ensemble of classifiers Accuracy BibRef

Roy, A., Cruz, R.M.O.[Rafael M.O.], Sabourin, R.[Robert], Cavalcanti, G.D.C.[George D.C.],
Meta-regression based pool size prediction scheme for dynamic selection of classifiers,
ICPR16(216-221)
IEEE DOI 1705
Complexity theory, Computational modeling, Data models, Force, Prediction algorithms, Predictive models, Training BibRef

Manjunath, G.[Geetha], Murty, M.N.[M. Narasimha], Sitaram, D.[Dinkar],
Combining heterogeneous classifiers for relational databases,
PR(46), No. 1, January 2013, pp. 317-324.
Elsevier DOI 1209
Heterogeneous classifier; RDF; Relational data; RDBMS BibRef

Carpineto, C.[Claudio], Romano, G.[Giovanni],
Consensus Clustering Based on a New Probabilistic Rand Index with Application to Subtopic Retrieval,
PAMI(34), No. 12, December 2012, pp. 2315-2326.
IEEE DOI 1210
BibRef

Jing, L.P.[Li-Ping], Ng, M.K.,
Sparse Label-Indicator Optimization Methods for Image Classification,
IP(23), No. 3, March 2014, pp. 1002-1014.
IEEE DOI 1403
computer vision BibRef

Sen, M.U.[Mehmet Umut], Erdogan, H.[Hakan],
Linear classifier combination and selection using group sparse regularization and hinge loss,
PRL(34), No. 3, 1 February 2013, pp. 265-274.
Elsevier DOI 1301
BibRef
Earlier: A2, A1:
A Unifying Framework for Learning the Linear Combiners for Classifier Ensembles,
ICPR10(2985-2988).
IEEE DOI 1008
Classifier combination; Group sparsity; Classifier selection; Regularized empirical risk minimization; Hinge loss BibRef

Audhkhasi, K., Narayanan, S.,
A Globally-Variant Locally-Constant Model for Fusion of Labels from Multiple Diverse Experts without Using Reference Labels,
PAMI(35), No. 4, April 2013, pp. 769-783.
IEEE DOI 1303
Both computer and people. BibRef

Parker, J.K.[Jonathon K.], Downs, J.A.[Joni A.],
Footprint generation using fuzzy-neighborhood clustering,
GeoInfo(17), No. 2, April 2013, pp. 285-299.
Springer DOI 1304
Expand on Density-Based Spatial Clustering with Noise (DBSCAN). BibRef

Hou, J.[Jian], Pelillo, M.[Marcello],
A simple feature combination method based on dominant sets,
PR(46), No. 11, November 2013, pp. 3129-3139.
Elsevier DOI 1306
Feature combination; Object classification; Dominant sets; Kernel accuracy BibRef

Hou, J.[Jian], Cui, H.X.[Hong-Xia],
Density Normalization in Density Peak Based Clustering,
GbRPR17(187-196).
Springer DOI 1706
BibRef

Hou, J.[Jian], Pelillo, M.[Marcello],
A new density kernel in density peak based clustering,
ICPR16(468-473)
IEEE DOI 1705
Clustering algorithms, Correlation, Data models, Density measurement, Estimation, Kernel, Shape BibRef

Lourenço, A.[André], Bulò, S.R.[Samuel Rota], Rebagliati, N.[Nicola], Fred, A.[Ana], Figueiredo, M.A.T.[Mário A.T.],
Consensus Clustering Using Partial Evidence Accumulation,
IbPRIA13(69-78).
Springer DOI 1307
BibRef

Sagha, H.[Hesam], Bayati, H.[Hamidreza], del R. Millán, J.[José], Chavarriaga, R.[Ricardo],
On-line anomaly detection and resilience in classifier ensembles,
PRL(34), No. 15, 2013, pp. 1916-1927.
Elsevier DOI 1309
Anomalous behaviors in an ensemble of classifiers by monitoring their decisions. BibRef

Hajdu, A., Hajdu, L., Jonas, A., Kovacs, L., Toman, H.,
Generalizing the Majority Voting Scheme to Spatially Constrained Voting,
IP(22), No. 11, 2013, pp. 4182-4194.
IEEE DOI 1310
Generalized majority voting BibRef

Hajdu, A., Tomán, H., Kovács, L., Hajdu, L.,
Composing ensembles by a stochastic approach under execution time constraint,
ICPR16(222-227)
IEEE DOI 1705
Approximation algorithms, Concrete, Linear programming, Optical imaging, Optimization, Retina, Time, factors BibRef

Franek, L.[Lucas], Jiang, X.Y.[Xiao-Yi],
Ensemble clustering by means of clustering embedding in vector spaces,
PR(47), No. 2, 2014, pp. 833-842.
Elsevier DOI 1311
Ensemble clustering BibRef

Le Capitaine, H.[Hoel],
A unified view of class-selection with probabilistic classifiers,
PR(47), No. 2, 2014, pp. 843-853.
Elsevier DOI 1311
Reject options BibRef

le Capitaine, H.[Hoel], Frélicot, C.[Carl],
A family of measures for best top-n class-selective decision rules,
PR(45), No. 1, 2012, pp. 552-562.
Elsevier DOI 1410
BibRef
Earlier:
On Selecting an Optimal Number of Clusters for Color Image Segmentation,
ICPR10(3388-3391).
IEEE DOI 1008
Reject options BibRef
And:
An Optimum Class-Rejective Decision Rule and Its Evaluation,
ICPR10(3312-3315).
IEEE DOI 1008
BibRef
Earlier:
A Family of Cluster Validity Indexes Based on a l-Order Fuzzy OR Operator,
SSPR08(612-621).
Springer DOI 0812
BibRef
And:
A class-selective rejection scheme based on blockwise similarity of typicality degrees,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhang, C.X.[Chun-Xia], Zhang, J.S.[Jiang-She], Ji, N.N.[Nan-Nan], Guo, G.[Gao],
Learning ensemble classifiers via restricted Boltzmann machines,
PRL(36), No. 1, 2014, pp. 161-170.
Elsevier DOI 1312
Ensemble classifier BibRef

Ji, N.N.[Nan-Nan], Zhang, J.S.[Jiang-She], Zhang, C.X.[Chun-Xia], Wang, L.[Lei],
Discriminative restricted Boltzmann machine for invariant pattern recognition with linear transformations,
PRL(45), No. 1, 2014, pp. 172-180.
Elsevier DOI 1407
Discriminative restricted Boltzmann machine BibRef

Read, J.[Jesse], Martino, L.[Luca], Luengo, D.[David],
Efficient monte carlo methods for multi-dimensional learning with classifier chains,
PR(47), No. 3, 2014, pp. 1535-1546.
Elsevier DOI 1312
Classifier chains BibRef

Read, J.[Jesse], Martino, L.[Luca], Olmos, P.M.[Pablo M.], Luengo, D.[David],
Scalable multi-output label prediction: From classifier chains to classifier trellises,
PR(48), No. 6, 2015, pp. 2096-2109.
Elsevier DOI 1503
Classifier chains BibRef

Toh, K.A.[Kar-Ann], Tan, G.C.[Geok-Choo],
Exploiting the relationships among several binary classifiers via data transformation,
PR(47), No. 3, 2014, pp. 1509-1522.
Elsevier DOI 1312
Binary classification BibRef

Jin, G.[Gaole], Raich, R.[Raviv],
Hinge loss bound approach for surrogate supervision multi-view learning,
PRL(37), No. 1, 2014, pp. 143-150.
Elsevier DOI 1402
Multi-view learning BibRef

Bartolucci, F.[Francesco], Pandolfi, S.[Silvia],
Comment on the paper 'On the memory complexity of the forward-backward algorithm,',
PRL(38), No. 1, 2014, pp. 15-19.
Elsevier DOI 1402
Hidden Markov model See also On the memory complexity of the forward-backward algorithm. BibRef

Berikov, V.[Vladimir],
Weighted ensemble of algorithms for complex data clustering,
PRL(38), No. 1, 2014, pp. 99-106.
Elsevier DOI 1402
Clustering BibRef

Ahmed, N.[Nasir], Jalil, A.[Abdul],
Multimode Image Clustering Using Optimal Image Descriptor,
IEICE(E97-D), No. 4, April 2014, pp. 743-751.
WWW Link. 1404
BibRef

Ahmed, N.[Nasir],
Image clustering using exponential discriminant analysis,
IET-CV(9), No. 1, 2015, pp. 1-12.
DOI Link 1504
image sampling. Regularization to handle small sample size BibRef

Ramzi, P.[Pouria], Samadzadegan, F.[Farhad], Reinartz, P.[Peter],
An AdaBoost Ensemble Classifier System for Classifying Hyperspectral Data,
PFG(2014), No. 1, February 2014, pp. 27-39.
DOI Link 1405
BibRef

Wang, X.Z.[Xi-Zhao], Wang, R.[Ran], Feng, H.M.[Hui-Min], Wang, H.C.[Hua-Chao],
A New Approach to Classifier Fusion Based on Upper Integral,
Cyber(44), No. 5, May 2014, pp. 620-635.
IEEE DOI 1405
fuzzy set theory BibRef

Li, L.J.[Lei-Jun], Hu, Q.H.[Qing-Hua], Wu, X.Q.[Xiang-Qian], Yu, D.[Daren],
Exploration of classification confidence in ensemble learning,
PR(47), No. 9, 2014, pp. 3120-3131.
Elsevier DOI 1406
Ensemble learning BibRef

Li, L.[Lin], Stolkin, R.[Rustam], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Wang, S.[Shuang],
A compressed sensing approach for efficient ensemble learning,
PR(47), No. 10, 2014, pp. 3451-3465.
Elsevier DOI 1406
Ensemble learning BibRef

Yu, Z.W.[Zhi-Wen], Li, L.[Le], Gao, Y.J.[Yun-Jun], You, J.[Jane], Liu, J.M.[Ji-Ming], Wong, H.S.[Hau-San], Han, G.Q.[Guo-Qiang],
Hybrid clustering solution selection strategy,
PR(47), No. 10, 2014, pp. 3362-3375.
Elsevier DOI 1406
Cluster ensemble BibRef

Liu, F., Zhang, W.,
TOPSIS-Based Consensus Model for Group Decision-Making With Incomplete Interval Fuzzy Preference Relations,
Cyber(44), No. 8, August 2014, pp. 1283-1294.
IEEE DOI 1407
Aggregates BibRef

Britto, Jr., A.S.[Alceu S.], Sabourin, R.[Robert], Oliveira, L.E.S.[Luiz E.S.],
Dynamic selection of classifiers: A comprehensive review,
PR(47), No. 11, 2014, pp. 3665-3680.
Elsevier DOI 1407
Ensemble of classifiers BibRef

Diao, R., Chao, F., Peng, T., Snooke, N., Shen, Q.,
Feature Selection Inspired Classifier Ensemble Reduction,
Cyber(44), No. 8, August 2014, pp. 1259-1268.
IEEE DOI 1407
Accuracy BibRef

Tepper, M.[Mariano], Sapiro, G.[Guillermo],
A Biclustering Framework for Consensus Problems,
SIIMS(7), No. 4, 2014, pp. 2488-2525.
DOI Link 1412
Merging solution from 2 clustering solutions. BibRef

Mao, S.[Shasha], Jiao, L.C.[Li-Cheng], Xiong, L.[Lin], Gou, S.P.[Shui-Ping], Chen, B.[Bo], Yeung, S.K.[Sai-Kit],
Weighted classifier ensemble based on quadratic form,
PR(48), No. 5, 2015, pp. 1688-1706.
Elsevier DOI 1502
Ensemble learning BibRef

Tsai, J.T.[Jeng-Tsung], Lin, Y.Y.[Yen-Yu], Liao, H.Y.M.,
Per-Cluster Ensemble Kernel Learning for Multi-Modal Image Clustering With Group-Dependent Feature Selection,
MultMed(16), No. 8, December 2014, pp. 2229-2241.
IEEE DOI 1502
feature selection BibRef

Guo, Y.W.[Yu-Wei], Jiao, L.C.[Li-Cheng], Wang, S.[Shuang], Wang, S.[Shuo], Liu, F.[Fang], Rong, K.X.[Kai-Xuan], Xiong, T.[Tao],
A novel dynamic rough subspace based selective ensemble,
PR(48), No. 5, 2015, pp. 1638-1652.
Elsevier DOI 1502
Rough set BibRef

Hayat, M.[Munawar], Bennamoun, M.[Mohammed], An, S.[Senjian],
Deep Reconstruction Models for Image Set Classification,
PAMI(37), No. 4, April 2015, pp. 713-727.
IEEE DOI 1503
BibRef
Earlier:
Learning Non-linear Reconstruction Models for Image Set Classification,
CVPR14(1915-1922)
IEEE DOI 1409
BibRef
Earlier:
Reverse Training: An Efficient Approach for Image Set Classification,
ECCV14(VI: 784-799).
Springer DOI 1408
Data models. Deep Learning; Face Recognition; Image Set Classification. Binary classifier, one class from all others. Extend binary to multi-class. BibRef

Shah, S.A.A., Nadeem, U., Bennamoun, M.[Mohammed], Sohel, F., Togneri, R.,
Efficient Image Set Classification Using Linear Regression Based Image Reconstruction,
Biometrics17(601-610)
IEEE DOI 1709
Euclidean distance, Image recognition, Image reconstruction, Image resolution, Machine learning, Training BibRef

Ammour, N.[Nassim], Alajlan, N.[Naif],
A dynamic weights OWA fusion for ensemble clustering,
SIViP(9), No. 3, March 2015, pp. 727-734.
WWW Link. 1503
BibRef

Zhong, C.M.[Cai-Ming], Yue, X.D.[Xiao-Dong], Zhang, Z.[Zehua], Lei, J.S.[Jing-Sheng],
A clustering ensemble: Two-level-refined co-association matrix with path-based transformation,
PR(48), No. 8, 2015, pp. 2699-2709.
Elsevier DOI 1505
Clustering BibRef

Nguyen, T.T.[Tien Thanh], Nguyen, T.T.T.[Thi Thu Thuy], Pham, X.C.[Xuan Cuong], Liew, A.W.C.[Alan Wee-Chung],
A novel combining classifier method based on Variational Inference,
PR(49), No. 1, 2016, pp. 198-212.
Elsevier DOI 1511
Ensemble method BibRef

Younsi, R.[Reda], Bagnall, A.[Anthony],
Ensembles of random sphere cover classifiers,
PR(49), No. 1, 2016, pp. 213-225.
Elsevier DOI 1511
Sphere cover BibRef

Gutierrez, P.[Patricia], Osman, N.[Nardine], Roig, C.[Carme], Sierra, C.[Carles],
Trust-based community assessment,
PRL(67, Part 1), No. 1, 2015, pp. 49-58.
Elsevier DOI 1511
Trust BibRef

Huang, D.[Dong], Lai, J.H.[Jian-Huang], Wang, C.D.[Chang-Dong],
Ensemble clustering using factor graph,
PR(50), No. 1, 2016, pp. 131-142.
Elsevier DOI 1512
Ensemble clustering BibRef

Xu, Y.M.[Yu-Meng], Wang, C.D.[Chang-Dong], Lai, J.H.[Jian-Huang],
Weighted Multi-view Clustering with Feature Selection,
PR(53), No. 1, 2016, pp. 25-35.
Elsevier DOI 1602
Data clustering BibRef

Cvetkovic, S.[Stevica], Stojanovic, M.B.[Miloš B.], Nikolic, S.V.[Saša V.],
Multi-channel descriptors and ensemble of Extreme Learning Machines for classification of remote sensing images,
SP:IC(39, Part A), No. 1, 2015, pp. 111-120.
Elsevier DOI 1512
Scene classification BibRef

Kim, K.[Kyounghoon], Lin, H.[Helin], Choi, J.Y.[Jin Young], Choi, K.Y.[Ki-Young],
A design framework for hierarchical ensemble of multiple feature extractors and multiple classifiers,
PR(52), No. 1, 2016, pp. 1-16.
Elsevier DOI 1601
Ensemble of detection systems BibRef

He, L.[Li], Zhang, H.[Hong],
Iterative ensemble normalized cuts,
PR(52), No. 1, 2016, pp. 274-286.
Elsevier DOI 1601
Iterative ensemble NCut BibRef

Fijalkow, I., Heiman, E., Messer, H.,
Parameter Estimation from Heterogeneous/Multimodal Data Sets,
SPLetters(23), No. 3, March 2016, pp. 390-393.
IEEE DOI 1603
distributed sensors BibRef

Shoari, A., Mateos, G., Seyedi, A.,
Analysis of Target Localization With Ideal Binary Detectors via Likelihood Function Smoothing,
SPLetters(23), No. 5, May 2016, pp. 737-741.
IEEE DOI 1604
Approximation error BibRef

Shoari, A., Mateos, G.,
On the Definition and Existence of a Minimum Variance Unbiased Estimator for Target Localization,
SPLetters(23), No. 7, July 2016, pp. 964-968.
IEEE DOI 1608
signal detection BibRef

Shoari, A., Seyedi, A.,
On Localization of A Non-Cooperative Target with Non-Coherent Binary Detectors,
SPLetters(21), No. 6, June 2014, pp. 746-750.
IEEE DOI 1404
Acoustic measurements BibRef

Cavalcanti, G.D.C.[George D.C.], Oliveira, L.S.[Luiz S.], Moura, T.J.M.[Thiago J.M.], Carvalho, G.V.[Guilherme V.],
Combining diversity measures for ensemble pruning,
PRL(74), No. 1, 2016, pp. 38-45.
Elsevier DOI 1604
Ensemble pruning BibRef

Bhardwaj, M.[Manju], Bhatnagar, V.[Vasudha], Sharma, K.[Kapil],
Cost-effectiveness of classification ensembles,
PR(57), No. 1, 2016, pp. 84-96.
Elsevier DOI 1605
Ensemble BibRef

Peng, Y., Zhou, X., Wang, D.Z., Patwa, I., Gong, D., Fang, C.V.,
Multimodal Ensemble Fusion for Disambiguation and Retrieval,
MultMedMag(23), No. 2, April 2016, pp. 42-52.
IEEE DOI 1605
Correlation BibRef

Santhanam, V.[Venkataraman], Morariu, V.I.[Vlad I.], Harwood, D.[David], Davis, L.S.[Larry S.],
A non-parametric approach to extending generic binary classifiers for multi-classification,
PR(58), No. 1, 2016, pp. 149-158.
Elsevier DOI 1606
Multi-classification BibRef

Chen, J.[Jike], Xia, J.[Junshi], Du, P.J.[Pei-Jun], Chanussot, J.[Jocelyn], Xue, Z.H.[Zhao-Hui], Xie, X.J.[Xiang-Jian],
Kernel Supervised Ensemble Classifier for the Classification of Hyperspectral Data Using Few Labeled Samples,
RS(8), No. 7, 2016, pp. 601.
DOI Link 1608
BibRef

Lin, L.[Liang], Corso, J.J.[Jason J.], Zuo, W.M.[Wang-Meng], Zhang, D.[David], Yao, B.Z.[Benjamin Z.],
Compositional models and Structured learning for visual recognition,
PR(59), No. 1, 2016, pp. 1-4.
Elsevier DOI 1609
BibRef

Yu, Z.W.[Zhi-Wen], Wang, D.X.[Da-Xing], You, J.[Jane], Wong, H.S.[Hau-San], Wu, S.[Si], Zhang, J.[Jun], Han, G.Q.[Guo-Qiang],
Progressive subspace ensemble learning,
PR(60), No. 1, 2016, pp. 692-705.
Elsevier DOI 1609
Ensemble learning BibRef

Lourenço, P.[Pedro], Guerreiro, B.J.[Bruno J.], Ponti, M.[Moacir], Kittler, J.V.[Josef V.], Riva, M.[Mateus], de Campos, T.[Teófilo], Zor, C.[Cemre],
A decision cognizant Kullback-Leibler divergence,
PR(61), No. 1, 2017, pp. 470-478.
Elsevier DOI 1705
Kullback-Leibler divergence BibRef

Zhao, Z.Q.[Zhi-Qiang], Jiao, L.C.[Li-Cheng], Zhao, J.[Jiaqi], Gu, J.[Jing], Zhao, J.[Jin],
Discriminant deep belief network for high-resolution SAR image classification,
PR(61), No. 1, 2017, pp. 686-701.
Elsevier DOI 1705
Discriminant feature learning BibRef

Denitto, M.[Matteo], Farinelli, A.[Alessandro], Figueiredo, M.A.T., Bicego, M.[Manuele],
A biclustering approach based on factor graphs and the max-sum algorithm,
PR(62), No. 1, 2017, pp. 114-124.
Elsevier DOI 1705
Biclustering BibRef

Denitto, M.[Matteo], Bicego, M.[Manuele], Farinelli, A.[Alessandro], Figueiredo, M.A.T.,
Spike and slab biclustering,
PR(72), No. 1, 2017, pp. 186-195.
Elsevier DOI 1708
Biclustering BibRef

Denitto, M.[Matteo], Magri, L., Farinelli, A.[Alessandro], Fusiello, A., Bicego, M.[Manuele],
Multiple Structure Recovery via Probabilistic Biclustering,
SSSPR16(274-284).
Springer DOI 1611
BibRef

Denitto, M.[Matteo], Farinelli, A.[Alessandro], Franco, G.[Giuditta], Bicego, M.[Manuele],
A Binary Factor Graph Model for Biclustering,
SSSPR14(394-403).
Springer DOI 1408
BibRef

Berikov, V.[Vladimir], Pestunov, I.[Igor],
Ensemble clustering based on weighted co-association matrices: Error bound and convergence properties,
PR(63), No. 1, 2017, pp. 427-436.
Elsevier DOI 1612
Weighted clustering ensemble BibRef

Pourtaheri, Z.K.[Zeinab Khatoun], Zahiri, S.H.[Seyed Hamid], Razavi, S.M.[Seyed Mohammad],
Design and Stability Analysis of Multi-Objective Ensemble Classifiers,
ELCVIA(15), No. 1, 2016, pp. 32-47.
DOI Link 1702
BibRef

Tian, C.W.[Chun-Wei], Sun, G.L.[Guang-Lu], Zhang, Q.[Qi], Wang, W.B.[Wei-Bing], Chen, T.[Teng], Sun, Y.[Yuan],
Integrating Sparse and Collaborative Representation Classifications for Image Classification,
IJIG(17), No. 02, 2017, pp. 1750007.
DOI Link 1704
BibRef

Zhao, X.W.[Xing-Wang], Liang, J.[Jiye], Dang, C.Y.[Chuang-Yin],
Clustering ensemble selection for categorical data based on internal validity indices,
PR(69), No. 1, 2017, pp. 150-168.
Elsevier DOI 1706
Clustering, ensemble, selection BibRef

Santucci, E.[Enrica], Didaci, L.[Luca], Fumera, G.[Giorgio], Roli, F.[Fabio],
A parameter randomization approach for constructing classifier ensembles,
PR(69), No. 1, 2017, pp. 1-13.
Elsevier DOI 1706
Multiple, classifier, systems BibRef

Sevilla-Villanueva, B.[Beatriz], Gibert, K.[Karina], Sànchez-Marrè, M.[Miquel],
A methodology to discover and understand complex patterns: Interpreted Integrative Multiview Clustering (I2MC),
PRL(93), No. 1, 2017, pp. 85-94.
Elsevier DOI 1706
Multiview Clustering BibRef

Jain, B.J.[Brijnesh J.],
Consistency of mean partitions in consensus clustering,
PR(71), No. 1, 2017, pp. 26-35.
Elsevier DOI 1707
Consensus, clustering BibRef

Oliveira, D.V.R.[Dayvid V.R.], Cavalcanti, G.D.C.[George D.C.], Sabourin, R.[Robert],
Online pruning of base classifiers for Dynamic Ensemble Selection,
PR(72), No. 1, 2017, pp. 44-58.
Elsevier DOI 1708
Ensemble of classifiers BibRef

Ailem, M.[Melissa], Role, F.[François], Nadif, M.[Mohamed],
Model-based co-clustering for the effective handling of sparse data,
PR(72), No. 1, 2017, pp. 108-122.
Elsevier DOI 1708
Mixture, models BibRef

Sublime, J.[Jérémie], Matei, B.[Basarab], Cabanes, G.[Guénaël], Grozavu, N.[Nistor], Bennani, Y.[Younès], Cornuéjols, A.[Antoine],
Entropy based probabilistic collaborative clustering,
PR(72), No. 1, 2017, pp. 144-157.
Elsevier DOI 1708
Collaborative clustering BibRef


Ren, X.[Xudie], Guo, H.N.[Hao-Nan], Li, S.H.[Sheng-Hong], Wang, S.L.[Shi-Lin], Li, J.H.[Jian-Hua],
A Novel Image Classification Method with CNN-XGBoost Model,
IWDW17(378-390).
Springer DOI 1708
Combining Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost). BibRef

Zhao, J., Zhang, Z., Chang, Z.J.[Zhen-Jun], Liu, D.J.[Dian-Jian],
Classifier ensemble with relevance-based feature subset selection,
ICIVC17(1137-1141)
IEEE DOI 1708
Cancer, Classification algorithms, Dermatology, Entropy, Ions, Iris recognition, Lead, classifier ensemble, feature subset selection, information entropy, relevance BibRef

Tayanov, V.[Vitaliy], Krzyzak, A.[Adam], Suen, C.[Ching],
Classification Boosting by Data Decomposition Using Consensus-Based Combination of Classifiers,
ICIAR17(408-415).
Springer DOI 1706
BibRef

Togban, E.[Elvis], Ziou, D.[Djemel],
Classification Using Mixture of Discriminative Learners: The Case of Compositional Data,
ICIAR17(416-425).
Springer DOI 1706
BibRef

Rakicevic, N., Rudovic, O., Petridis, S., Pantic, M.,
Multi-modal Neural Conditional Ordinal Random Fields for agreement level estimation,
ICPR16(2228-2233)
IEEE DOI 1705
Data models, Estimation, Feature extraction, Hidden Markov models, Optimization, Standards, Visualization BibRef

Goswami, G., Ratha, N., Singh, R., Vatsa, M.,
Improving classifier fusion via Pool Adjacent Violators normalization,
ICPR16(1011-1016)
IEEE DOI 1705
Algorithm design and analysis, Biological system modeling, Biometrics (access control), Calibration, Clustering algorithms, Receivers, Robustness BibRef

Jhuo, I.H., Weng, L., Cheng, W.H., Lee, D.T.,
A feature fusion framework for hashing,
ICPR16(2288-2293)
IEEE DOI 1705
Approximation algorithms, Feature extraction, Measurement, Multimedia communication, Multimedia databases, Robustness BibRef

Yildiz, O.T.[Olcay Taner], Ulas, A.[Aydin],
Incremental construction of rule ensembles using classifiers produced by different class orderings,
ICPR16(492-497)
IEEE DOI 1705
Computers, Feature extraction, Optimization, Pattern recognition, Search problems, Technological innovation, Training, Ensemble construction, Rule, sets BibRef

Quan, Y.H.[Yu-Hui], Xu, Y.[Yong], Sun, Y.P.[Yu-Ping], Huang, Y.[Yan], Ji, H.[Hui],
Sparse Coding for Classification via Discrimination Ensemble,
CVPR16(5839-5847)
IEEE DOI 1612
BibRef

Luan, W.T.[Wen-Tao], Yang, Y.Z.[Ye-Zhou], Fermüller, C.[Cornelia], Baras, J.S.[John S.],
Reliable Attribute-Based Object Recognition Using High Predictive Value Classifiers,
ECCV16(III: 801-815).
Springer DOI 1611
object recognition in 3D using an ensemble of attribute-based classifiers. BibRef

Rai, N., Negi, S., Chaudhury, S., Deshmukh, O.,
Partial Multi-View Clustering using Graph Regularized NMF,
ICPR16(2192-2197)
IEEE DOI 1705
Algorithm design and analysis, Clustering algorithms, Clustering methods, Laplace equations, Mathematical model, Nickel, Optimization BibRef

Gandhi, A.[Ankit], Sharma, A.[Arjun], Biswas, A.[Arijit], Deshmukh, O.[Om],
GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion,
CVAVM16(II: 883-899).
Springer DOI 1611
BibRef

Rahimpour, A.[Alireza], Taalimi, A.[Ali], Luo, J.J.[Jia-Jia], Qi, H.R.[Hai-Rong],
Distributed object recognition in smart camera networks,
ICIP16(669-673)
IEEE DOI 1610
Local decisiongs, final based on majority vote. Base stations BibRef

Lad, S., Paredes, B.R., Valentin, J., Torr, P., Parikh, D.,
Knowing who to listen to: Prioritizing experts from a diverse ensemble for attribute personalization,
ICIP16(4463-4467)
IEEE DOI 1610
Adaptation models BibRef

Fawzi, A.[Alhussein], Frossard, P.[Pascal],
Manitest: Are classifiers really invariant?,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Feng, W.[Wei], Boukir, S.[Samia],
Class noise removal and correction for image classification using ensemble margin,
ICIP15(4698-4702)
IEEE DOI 1512
Class noise removal BibRef

Seib, V.[Viktor], Memmesheimer, R.[Raphael], Paulus, D.[Dietrich],
Ensemble classifier for joint object instance and category recognition on RGB-D data,
ICIP15(143-147)
IEEE DOI 1512
category and instance recognition; ensemble classifier; mobile robots BibRef

Chakeri, A.[Alireza], Hall, L.O.[Lawrence O.],
Dominant Sets as a Framework for Cluster Ensembles: An Evolutionary Game Theory Approach,
ICPR14(3457-3462)
IEEE DOI 1412
Clustering algorithms BibRef

Dumonceaux, F., Raschia, G., Gelgon, M.,
An Algebraic Approach to Ensemble Clustering,
ICPR14(1301-1306)
IEEE DOI 1412
Aggregates; Algebra; Calculus; Context; Lattices; Semantics; Upper bound BibRef

Yang, C.[Chun], Yin, X.C.[Xu-Cheng], Hao, H.W.[Hong-Wei],
Diversity-Based Ensemble with Sample Weight Learning,
ICPR14(1236-1241)
IEEE DOI 1412
Accuracy BibRef

Bagheri, M.A.[Mohammad Ali], Gao, Q.G.[Qi-Gang], Escalera, S.[Sergio],
Generic Subclass Ensemble: A Novel Approach to Ensemble Classification,
ICPR14(1254-1259)
IEEE DOI 1412
Accuracy BibRef

Eeti, L.N.[Laxmi Narayana], Buddhiraju, K.M.[Krishna Mohan],
Perspective Based Model for Constructing Diverse Ensemble Members in Multi-classifier Systems for Multi-spectral Image Classification,
CIARP14(637-644).
Springer DOI 1411
BibRef

López-Caloca, A.A.[Alejandra A.],
Data Fusion Approach for Employing Multiple Classifiers to Improve Lake Shoreline Analysis,
CASI14(1022-1029).
Springer DOI 1411
BibRef

Nowozin, S.[Sebastian],
Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case,
CVPR14(548-555)
IEEE DOI 1409
decision theory; structured prediction BibRef

Morvant, E.[Emilie], Habrard, A.[Amaury], Ayache, S.[Stéphane],
Majority Vote of Diverse Classifiers for Late Fusion,
SSSPR14(153-162).
Springer DOI 1408
BibRef

Ma, T.Y.[Tian-Yang], Oh, S.[Sangmin], Perera, A., Latecki, L.J.,
Learning Non-linear Calibration for Score Fusion with Applications to Image and Video Classification,
LSVSM13(323-330)
IEEE DOI 1403
calibration BibRef

Weng, C.Q.[Chao-Qun], Wang, H.X.[Hong-Xing], Yuan, J.S.[Jun-Song],
Learning weighted geometric pooling for image classification,
ICIP13(3805-3809)
IEEE DOI 1402
joint pooling and classification BibRef

Yan, Y.[Yan], Yin, X.C.[Xu-Cheng], Wang, Z.B.[Zhi-Bin], Yin, X.W.[Xu-Wang], Yang, C.[Chun], Hao, H.W.[Hong-Wei],
Sorting-Based Dynamic Classifier Ensemble Selection,
ICDAR13(673-677)
IEEE DOI 1312
learning (artificial intelligence) BibRef

Mahmoudi, F.T.[F. Tabib], Samadzadegan, F., Reinartz, P.,
A Decision Level Fusion Method for Object Recognition Using Multi-Angular Imagery,
SMPR13(409-414).
HTML Version. 1311
BibRef

Campos, Y.[Yoisel], Estrada, R.[Roberto], Morell, C.[Carlos], Ferri, F.J.[Francesc J.],
A Feature Set Decomposition Method for the Construction of Multi-classifier Systems Trained with High-Dimensional Data,
CIARP13(I:278-285).
Springer DOI 1311
BibRef

Tu, W.T.[Wen-Ting], Sun, S.L.[Shi-Liang],
Dynamical ensemble learning with model-friendly classifiers for domain adaptation,
ICPR12(1181-1184).
WWW Link. 1302
BibRef

Krijthe, J.H.[Jesse H.], Ho, T.K.[Tin Kam], Loog, M.[Marco],
Improving cross-validation based classifier selection using meta-learning,
ICPR12(2873-2876).
WWW Link. 1302
BibRef

Kovalenko, D.[Dmitry], Srihari, S.N.[Sargur N.],
On methods for incorporating evidences into posterior scoring of hypotheses,
ICPR12(577-580).
WWW Link. 1302
BibRef

Sánchez-Vega, F.[Francisco], Eisner, J.[Jason], Younes, L.[Laurent], Geman, D.[Donald],
Learning Multivariate Distributions by Competitive Assembly of Marginals,
PAMI(35), No. 2, February 2013, pp. 398-410.
IEEE DOI 1301
motivated by compositional models and Bayesian networks, for small sample sizes. BibRef

Matikainen, P.[Pyry], Sukthankar, R.[Rahul], Hebert, M.[Martial],
Classifier Ensemble Recommendation,
WebScale12(I: 209-218).
Springer DOI 1210
BibRef

Sánchez, J.[Jorge], Redolfi, J.[Javier],
Classifier Combination Using Random Walks on the Space of Concepts,
CIARP12(789-796).
Springer DOI 1209
BibRef

Duval-Poo, M.A.[Miguel A.], Sosa-García, J.[Joan], Guerra-Gandón, A.[Alejandro], Vega-Pons, S.[Sandro], Ruiz-shulcloper, J.[José],
A New Classifier Combination Scheme Using Clustering Ensemble,
CIARP12(154-161).
Springer DOI 1209
BibRef

Nanda, A., Pujari, A.K.,
Weighted Co-clustering Based Clustering Ensemble,
NCVPRIPG11(46-49).
IEEE DOI 1205
BibRef

López, E.[Erick], Allende, H.[Héctor], Allende-Cid, H.[Héctor],
A Machine Learning Method for High-Frequency Data Forecasting,
CIARP14(621-628).
Springer DOI 1411
BibRef

Ñanculef, R.[Ricardo], López, E.[Erick], Allende, H.[Héctor], Allende-Cid, H.[Héctor],
An Ensemble Method for Incremental Classification in Stationary and Non-stationary Environments,
CIARP11(541-548).
Springer DOI 1111
BibRef

Rajasekhara, P., Pujari, A.K.[Arun K.],
A new clusterwise similarity for partitions based on quantitative disagreement,
ICCVGIP10(117-123).
DOI Link 1111
Get consnesus clustering. BibRef

Cai, X.[Xiao], Nie, F.P.[Fei-Ping], Cai, W.D.[Wei-Dong], Huang, H.[Heng],
Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model,
ICCV13(1737-1744)
IEEE DOI 1403
Heterogeneous Data Integration BibRef

Cai, X.[Xiao], Nie, F.P.[Fei-Ping], Huang, H.[Heng], Kamangar, F.[Farhad],
Heterogeneous image feature integration via multi-modal spectral clustering,
CVPR11(1977-1984).
IEEE DOI 1106
To combine the various features (SIFT, HOG, GIST, LBP, CENTRIST) using MMSC. BibRef

Ota, T.[Takahiro], Wada, T.[Toshikazu], Nakamura, T.[Takayuki],
Classifier Acceleration by Imitation,
ACCV10(IV: 653-664).
Springer DOI 1011
Classifier Molding. Imitate arbirtary classifiers by linear regression trees. BibRef

Vega-Pons, S.[Sandro], Ruiz-Shulcloper, J.[José],
Partition Selection Approach for Hierarchical Clustering Based on Clustering Ensemble,
CIARP10(525-532).
Springer DOI 1011
BibRef

Wandekokem, E.D.[Estefhan Dazzi], Varejão, F.M.[Flávio M.], Rauber, T.W.[Thomas W.],
An Overproduce-and-Choose Strategy to Create Classifier Ensembles with Tuned SVM Parameters Applied to Real-World Fault Diagnosis,
CIARP10(500-508).
Springer DOI 1011
BibRef

Yuksel, S.E.[Seniha Esen], Gader, P.D.[Paul D.],
Variational Mixture of Experts for Classification with Applications to Landmine Detection,
ICPR10(2981-2984).
IEEE DOI 1008
BibRef

Armano, G.[Giuliano], Hatami, N.[Nima],
Random Prototype-based Oracle for Selection-fusion Ensembles,
ICPR10(77-80).
IEEE DOI 1008
BibRef

Abdala, D.D.[Daniel Duarte], Wattuya, P.[Pakaket], Jiang, X.Y.[Xiao-Yi],
Ensemble Clustering via Random Walker Consensus Strategy,
ICPR10(1433-1436).
IEEE DOI 1008
BibRef

Senko, O.V.[Oleg V.], Kuznetsova, A.V.[Anna V.],
Pattern Recognition Method Using Ensembles of Regularities Found by Optimal Partitioning,
ICPR10(2957-2960).
IEEE DOI 1008
BibRef

Kim, T.K.[Tae-Kyun], Woodley, T.[Thomas], Stenger, B.[Bjorn], Stenger, B.[Björn], Cipolla, R.[Roberto],
Online multiple classifier boosting for object tracking,
OLCV10(1-6).
IEEE DOI 1006
BibRef

Takahashi, T.[Tetsuji], Kudo, M.[Mineichi], Nakamura, A.[Atsuyoshi],
Classifier Selection in a Family of Polyhedron Classifiers,
CIARP09(441-448).
Springer DOI 0911
BibRef

Duin, R.P.W.[Robert P. W.], Pekkalska, E.[Elzbieta],
The Dissimilarity Representation for Structural Pattern Recognition,
CIARP11(1-24).
Springer DOI 1111
BibRef

Kim, S.W.[Sang-Woon], Duin, R.P.W.[Robert P.W.],
Dissimilarity-Based Classifications in Eigenspaces,
CIARP11(425-432).
Springer DOI 1111
BibRef
Earlier:
On Improving Dissimilarity-Based Classifications Using a Statistical Similarity Measure,
CIARP10(418-425).
Springer DOI 1011
BibRef
Earlier:
A Combine-Correct-Combine Scheme for Optimizing Dissimilarity-Based Classifiers,
CIARP09(425-432).
Springer DOI 0911
BibRef

Liu, Y.[Yu], Xu, X.J.[Xiu-Juan], Wang, C.[Chunyu],
Simple Ensemble of Extreme Learning Machine,
CISP09(1-5).
IEEE DOI 0910
BibRef

Li, J.J.[Jian-Jun], Wei, Z.H.[Zhi-Hui], Zhang, Z.J.[Zheng-Jun], Xie, J.C.[Jian-Chun],
Edge Detection Method Based on Multi-Expert Information Fusion,
CISP09(1-4).
IEEE DOI 0910
BibRef

Ranganathan, A.[Ananth],
Semantic Scene Segmentation using Random Multinomial Logit,
BMVC09(xx-yy).
PDF File. 0909
General multi-class classifier based on an ensemble of multinomial logistic regression models. BibRef

Yin, X.C.[Xu-Cheng], Hao, H.W.[Hong-Wei], Tang, Y.F.[Yun-Feng], Sun, J.[Jun], Naoi, S.[Satoshi],
Rejection Strategies with Multiple Classifiers for Handwritten Character Recognition,
ICDAR09(1126-1130).
IEEE DOI 0907
BibRef

Du, P., Sun, H., Zhang, W.,
Multiple classifier combination for target identification from high resolution remote sensing image,
HighRes09(xx-yy).
PDF File. 0906
BibRef

Sun, Y.[Ying], Qi, H.R.[Hai-Rong],
Dynamic target classification in wireless sensor networks,
ICPR08(1-4).
IEEE DOI 0812
Combining results from different sensors BibRef

Gupta, U.[Upavan], Ranganathan, N.[Nagarajan],
A microeconomic approach to multi-objective spatial clustering,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Uchida, S.[Seiichi], Amamoto, K.[Kazuma],
Early recognition of sequential patterns by classifier combination,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Karnick, M.[Matthew], Muhlbaier, M.D.[Michael D.], Polikar, R.[Robi],
Incremental learning in non-stationary environments with concept drift using a multiple classifier based approach,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Yu, Z.W.[Zhi-Wen], Deng, Z.K.[Zhong-Kai], Wong, H.S.[Hau-San],
Identification of phosphorylation sites using a hybrid classifier ensemble approach,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Byun, B.K.[Byung-Ki], Ma, C.Y.[Cheng-Yuan], Lee, C.H.[Chin-Hui],
An experimental study on discriminative concept classifier combination for TRECVID high-level feature extraction,
ICIP08(2532-2535).
IEEE DOI 0810
BibRef

Socorro, R.[Raisa], Micó, L.[Luisa],
Use of Structured Pattern Representations for Combining Classifiers,
SSPR08(811-820).
Springer DOI 0812
BibRef

Orrite, C.[Carlos], Rodríguez, M.[Mario], Martínez, F.[Francisco], Fairhurst, M.[Michael],
Classifier Ensemble Generation for the Majority Vote Rule,
CIARP08(340-347).
Springer DOI 0809
BibRef

Ozay, M.[Mete], Vural, F.T.Y.[Fatos Tunay Yarman],
On the Performance of Stacked Generalization Classifiers,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef

Maiti, C.[Chinmay], Pal, S.[Somnath],
Efficient Multi-method Rule Learning for Pattern Classification Machine Learning and Data Mining,
PReMI07(324-331).
Springer DOI 0712
BibRef

Kankanala, L.[Laxmi], Murty, M.N.[M. Narasimha],
Hybrid Approaches for Clustering,
PReMI07(25-32).
Springer DOI 0712
BibRef

Kyrgyzov, I.O.[Ivan O.], Maitre, H.[Henri], Campedel, M.[Marine],
A Method of Clustering Combination Applied to Satellite Image Analysis,
CIAP07(81-86).
IEEE DOI 0709
BibRef

Dimililer, N.[Nazife], Varoglu, E.[Ekrem], Altonçay, H.[Hakan],
Vote-Based Classifier Selection for Biomedical NER Using Genetic Algorithms,
IbPRIA07(II: 202-209).
Springer DOI 0706
BibRef

Valdovinos, R.M., Sánchez, J.S., Gasca, E.,
Influence of Resampling and Weighting on Diversity and Accuracy of Classifier Ensembles,
IbPRIA07(II: 250-257).
Springer DOI 0706
BibRef

Valdovinos, R.M., Sánchez, J.S.,
Performance Analysis of Classifier Ensembles: Neural Networks Versus Nearest Neighbor Rule,
IbPRIA07(I: 105-112).
Springer DOI 0706
BibRef

Mazón, J.N.[Jose-Norberto], Micó, L.[Luisa], Moreno-Seco, F.[Francisco],
New Neighborhood Based Classification Rules for Metric Spaces and Their Use in Ensemble Classification,
IbPRIA07(I: 354-361).
Springer DOI 0706
BibRef

Raudys, S.J.[Sarunas J.],
Generalization Error of Multinomial Classifier,
SSPR06(502-511).
Springer DOI 0608
BibRef

Raudys, S.J.[Sarunas J.], Denisov, V.[Vitalij], Bielskis, A.A.[Antanas Andrius],
A Pool of Classifiers by SLP: A Multi-class Case,
ICIAR06(II: 47-56).
Springer DOI 0610
BibRef

Andra, S.[Srinivas], Nagy, G.[George],
Combining Dichotomizers for MAP Field Classification,
ICPR06(IV: 210-214).
IEEE DOI 0609
BibRef

Viswanath, P., Jayasurya, K.[Karthik],
A Fast and Efficient Ensemble Clustering Method,
ICPR06(II: 720-723).
IEEE DOI 0609
BibRef

Lefaucheur, P.[Patrice], Nock, R.[Richard],
Robust Multiclass Ensemble Classifiers via Symmetric Functions,
ICPR06(IV: 136-139).
IEEE DOI 0609
BibRef

Ekbal, A.[Asif], Bandyopadhyay, S.[Sivaji],
Improving the Performance of a NER System by Post-processing and Voting,
SSPR08(831-841).
Springer DOI 0812
BibRef

Ekbal, A.[Asif],
Improvement of Prediction Accuracy Using Discretization and Voting Classifier,
ICPR06(II: 695-698).
IEEE DOI 0609
BibRef

Bauckhage, C.[Christian], Kaster, T.[Thomas],
Benefits of Separable, Multilinear Discriminant Classification,
ICPR06(III: 1240-1243).
IEEE DOI 0609
BibRef
And: ICPR06(IV: 959).
IEEE DOI 0609
BibRef

Bauckhage, C.[Christian], Kaster, T.[Thomas], Tsotsos, J.K.[John K.],
Applying Ensembles of Multilinear Classifiers in the Frequency Domain,
CVPR06(I: 95-102).
IEEE DOI 0606
BibRef

Bauckhage, C.[Christian], Tsotsos, J.K.[John K.],
Separable Linear Discriminant Classification,
DAGM05(318).
Springer DOI 0509
BibRef
And:
Separable Linear Classifiers for Online Learning in Appearance Based Object Detection,
CAIP05(347).
Springer DOI 0509
BibRef

Chellapilla, K.[Kumar], Shilman, M.[Michael], Simard, P.Y.[Patrice Y.],
Combining Multiple Classifiers for Faster Optical Character Recognition,
DAS06(358-367).
Springer DOI 0602
BibRef

Singh, R.[Rohit], Samal, S.[Sandeep], Lahiri, T.[Tapobrata],
A Novel Strategy for Designing Efficient Multiple Classifier,
ICB06(713-720).
Springer DOI 0601
BibRef

Valdovinos, R.M.[Rosa M.], Salvador-Sánchez, J., Barandela, R.[Ricardo],
Dynamic and Static Weighting in Classifier Fusion,
IbPRIA05(II:59).
Springer DOI 0509
BibRef

Beattie, M.[Michael], Vijaya Kumar, B.V.K., Lucey, S.[Simon], Tonguz, O.K.[Ozan K.],
Combining Verification Decisions in a Multi-vendor Environment,
AVBPA05(406).
Springer DOI 0509
BibRef

Gutierrez, J., Rouas, J.L., Andre-Obrecht, R.,
Weighted loss functions to make risk-based language identification fused decisions,
ICPR04(II: 863-866).
IEEE DOI 0409
BibRef

Reiter, S., Rigoll, G.,
Segmentation and classification of meeting events using multiple classifier fusion and dynamic programming,
ICPR04(III: 434-437).
IEEE DOI 0409
BibRef

Jaeger, S.,
Informational classifier fusion,
ICPR04(I: 216-219).
IEEE DOI 0409
BibRef

Yi, X.[Xing], Kou, Z.B.[Zhong-Bao], Zhang, C.S.[Chang-Shui],
Classifer combination based on active learning,
ICPR04(I: 184-187).
IEEE DOI 0409
BibRef

Dmitry, V., Dmitry, K.,
Data dependent classifer fusion for construction of stable effective algorithms,
ICPR04(I: 144-147).
IEEE DOI 0409
BibRef

Altinçay, H.[Hakan], Çizili, B.[Buket],
Classifier Combination through Clustering in the Output Spaces,
CAIP03(487-493).
Springer DOI 0311
BibRef

de Stefano, C.[Claudio], Fontanella, F.[Francesco], di Freca, A.S.[Alessandra Scotto], Marcelli, A.[Angelo],
Learning Bayesian Networks by Evolution for Classifier Combination,
ICDAR09(966-970).
IEEE DOI 0907
BibRef

de Stefano, C.[Claudio], d'Elia, C.[Ciro], Marcelli, A.[Angelo], di Freca, A.S.[Alessandra Scotto],
Using Bayesian Network for combining classifiers,
CIAP07(73-80).
IEEE DOI 0709
BibRef

de Stefano, C., della Cioppa, A., Marcelli, A.,
Exploiting reliability for dynamic selection of classifiers by means of genetic algorithms,
ICDAR03(671-675).
IEEE DOI 0311
BibRef
Earlier:
An adaptive weighted majority vote rule for combining multiple classifiers,
ICPR02(II: 192-195).
IEEE DOI 0211
BibRef

Soto, A.,
A Probabilistic Approach for the Adaptive Integration of Multiple Visual Cues Using an Agent Framework,
CMU-RI-TR-02-30, October, 2002. BibRef 0210 Ph.D.Thesis
HTML Version. 0306
BibRef

Sirlantzis, K., Fairhurst, M.C., Guest, R.M.,
An evolutionary algorithm for classifier and combination rule selection in multiple classifier systems,
ICPR02(II: 771-774).
IEEE DOI 0211
BibRef

Sirlantzis, K., Fairhurst, M.C.,
Optimisation of Multiple Classifier Systems Using Genetic Algorithms,
ICIP01(I: 1094-1097).
IEEE DOI 0108
BibRef
And:
Investigation of a novel self-configurable multiple classifier system for character recognition,
ICDAR01(1002-1006).
IEEE DOI 0109
BibRef

Tax, D.M.J., Duin, R.P.W.,
Using two-class classifiers for multiclass classification,
ICPR02(II: 124-127).
IEEE DOI 0211
BibRef
Earlier:
Data Description in Subspaces,
ICPR00(Vol II: 672-675).
IEEE DOI 0009
BibRef

Skurichina, M., Ypma, A., Duin, R.P.W.,
The Role of Subclasses in Machine Diagnostics,
ICPR00(Vol II: 668-671).
IEEE DOI 0009
BibRef

Jeong, S.H., Lim, K.T., Nam, Y.S.,
A combination method of two classifiers based on the information of confusion matrix,
FHR02(519-523).
IEEE Top Reference. 0209
BibRef

Mahamud, S.[Shyjan], Hebert, M.[Martial], Lafferty, J.[John],
Combining Simple Discriminators for Object Discrimination,
ECCV02(III: 776 ff.).
Springer DOI 0205
BibRef

Qian, Y., Suen, C.Y.,
Clustering Combination Method,
ICPR00(Vol II: 732-735).
IEEE DOI 0009
BibRef

Mascarilla, L., Frélicot, C.,
Another Look at Combining Rejection-based Pattern Classifiers,
ICPR00(Vol II: 156-159).
IEEE DOI 0009
BibRef

DeCarlo, D.[Douglas], Metaxas, D.N.[Dimitris N.],
Combining Information using Hard Constraints,
CVPR99(II: 132-138).
IEEE DOI Use hard constraints rather than statistical combination. BibRef 9900

Kang, H.J., Kim, J.H.,
A Probabilistic Framework for Combining Multiple Classifiers at Abstract Level,
ICDAR97(870-874).
IEEE DOI 9708
BibRef

Franke, J., Mandler, E.,
A comparison of two approaches for combining the votes of cooperating classifiers,
ICPR92(II:611-614).
IEEE DOI 9208
BibRef

Wong, G., Frei, H.P.,
Object recognition: the utopian method is dead; the time for combining simple methods has come,
ICPR92(III:185-188).
IEEE DOI 9208
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Bagging, Combinations, Classifiers .


Last update:Sep 18, 2017 at 11:34:11