14.1.5.2.3 Classifier Combination, Evaluation, Overview, Appliction Specific

Chapter Contents (Back)
Combination. Classifer Combinations.

Boland, P.J.,
Majority Systems and the Condorcet Jury Theorem,
Statistician(38), 1989, pp. 181-189. For independent classifiers, error eat less than .5, for an odd number of classifiers, majority voting increases the correct decision rate as the number of classifiers increases. BibRef 8900

Xu, L., Krzyzak, A., Suen, C.Y.,
Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition,
SMC(22), No. 3, 1992, pp. 418-435. Majority Voting for combinations. Unanimous consensus. Threshold Voting. Averaged Bayes Classifier. Dempster-Shafer. BibRef 9200

Xu, L.[Lei], Krzyzak, A., Oja, E.,
Unsupervised and supervised classifications by rival penalized competitive learning,
ICPR92(II:496-499).
IEEE DOI 9208
BibRef

Lam, L., Suen, C.Y.,
Application of Majority Voting to Pattern Recognition: An Analysis of Its Behavior and Performance,
SMC(27), No. 5, 1997, pp. 553-568. BibRef 9700
Earlier:
A Theoretical Analysis of the Application of Majority Voting to Pattern Recognition,
ICPR94(B:418-420).
IEEE DOI BibRef

Smyth, P.,
Bounds on the Mean Classification Error Rate of Multiple Experts,
PRL(17), No. 12, October 25 1996, pp. 1253-1257. 9612
BibRef

Kittler, J.V.[Joseph V.],
Combining Classifiers: A Theoretical Framework,
PAA(1), No. 1, 1998, pp. 18-27. BibRef 9800

Kittler, J.V., Hatef, M., Duin, R.P.W., Matas, J.,
On Combining Classifiers,
PAMI(20), No. 3, March 1998, pp. 226-239.
IEEE DOI 9805
The combination rule using the most restrictive assumptions, the sum rule, did best. Compared versions of Max, Median, Majority vote rule dreived from See also Improving Model Accuracy Using Optimal Linear Combinations of Trained Neural Networks. Min rule derived from See also Multistage Algorithm for Fast Classification of Patterns, A. Also Sum and Product rules. BibRef

Kittler, J.V., Duin, R.P.W., Hatef, M.,
Combining Classifiers,
ICPR96(II: 897-901).
IEEE DOI 9608
(Univ. of Surrey, UK) BibRef

Wang, L.C., Der, S.Z., Nasrabadi, N.M.,
Composite Classifiers for Automatic Target Recognition,
OptEng(37), No. 3, March 1998, pp. 858-868. 9804
BibRef

Wang, L.C., Chan, L., Nasrabadi, N.M., and Der, S.Z.,
Combination of Two Learning Algorithms for Automatic Target Recognition,
ICIP97(I: 881-884).
IEEE DOI BibRef 9700

Ng, G.S., Singh, H.,
Data Equalization with Evidence Combination for Pattern Recognition,
PRL(19), No. 3-4, March 1998, pp. 227-235. 9807
BibRef

Tax, D.M.J.[David M.J.], van Breukelen, M.[Martijn], Duin, R.P.W.[Robert P.W.], Kittler, J.V.[Josef V.],
Combining multiple classifiers by averaging or by multiplying?,
PR(33), No. 9, September 2000, pp. 1475-1485.
WWW Link. 0005
BibRef

van Breukelen, M.[Martijn], Duin, R.P.W.[Robert P.W.],
Neural Network Initialization by Combined Classifiers,
ICPR98(Vol I: 215-218).
IEEE DOI 9808
BibRef

Pudil, P., Novovicova, J., Blaha, S., Kittler, J.V.,
Multistage Pattern Recognition with Reject Option,
ICPR92(II:92-95).
IEEE DOI BibRef 9200

Kittler, J.V., Hojjatoleslami, A., Windeatt, T.,
Strategies for Combining Classifiers Employing Shared and Distinct Pattern Representations,
PRL(18), No. 11-13, November 1997, pp. 1373-1377. 9806
BibRef
Earlier:
Weighting Factors in Multiple Expert Fusion,
BMVC97(xx-yy).
HTML Version. 0209
BibRef

Hojjatoleslami, A., Kittler, J.V.[Josef V.],
Strategies for Weighted Combination of Classifiers Employing Shared and Distinct Pattern Representations,
ICPR98(Vol I: 338-340).
IEEE DOI 9808
BibRef

Kittler, J.V.[Josef V.], Hojjatoleslami, A.[Ali],
A Weighted Combination of Classifiers Employing Shared and Distinct Representations,
CVPR98(924-929).
IEEE DOI BibRef 9800

Alkoot, F.M., Kittler, J.V.,
Experimental Evaluation of Expert Fusion Strategies,
PRL(20), 1999, pp. 1361-1369. BibRef 9900
And:
Improving the Performance of the Product Fusion Strategy,
ICPR00(Vol II: 164-167).
IEEE DOI 0009
Compared Minimum, Maximum, Average, Median, Majority. Futher analysis in: See also Theoretical Study on Six Classifier Fusion Strategies, A. BibRef

Kittler, J.V., Alkoot, F.M.,
Sum versus vote fusion in multiple classifier systems,
PAMI(25), No. 1, January 2003, pp. 110-115.
IEEE DOI 0301
Analysis of fusion rules. BibRef

Petrakos, M., Benediktsson, J.A., Kanellopoulos, I.,
The effect of classifier agreement on the accuracy of the combined classifier in decision level fusion,
GeoRS(39), No. 11, November 2001, pp. 2539-2546.
IEEE Top Reference. 0111
BibRef
And: Correction: GeoRS(40), No. 1, January 2002, pp. 228-228.
IEEE Top Reference. 0203
BibRef

Briem, G.J., Benediktsson, J.A., Sveinsson, J.R.,
Multiple classifiers applied to multisource remote sensing data,
GeoRS(40), No. 10, October 2002, pp. 2291-2299.
IEEE Top Reference. 0301
BibRef

Benediktsson, J.A., Sveinsson, J.R.,
Multisource remote sensing data classification based on consensus and pruning,
GeoRS(41), No. 4, April 2003, pp. 932-936.
IEEE Abstract. 0307
See also Classification of Hyperspectral Data From Urban Areas Based on Extended Morphological Profiles. BibRef

Waske, B., Benediktsson, J.A.,
Fusion of Support Vector Machines for Classification of Multisensor Data,
GeoRS(45), No. 12, December 2007, pp. 3858-3866.
IEEE DOI 0711
BibRef

Martínez Trinidad, J.F.[José Francisco], Guzmán Arenas, A.[Adolfo],
The logical combinatorial approach to pattern recognition, an overview through selected works,
PR(34), No. 4, April 2001, pp. 741-751.
WWW Link. 0101
BibRef

Chen, D.C.[De-Chang], Cheng, X.Z.[Xiu-Zhen],
An asymptotic analysis of some expert fusion methods,
PRL(22), No. 8, June 2001, pp. 901-904.
Elsevier DOI 0105
BibRef

Kuncheva, L.I.[Ludmila I.],
Switching between selection and fusion in combining classifiers: An Experiment,
SMC-B(32), No. 2, April 2002, pp. 146-156.
IEEE Top Reference. 0205
BibRef

Kuncheva, L.I.[Ludmila I.],
Using diversity measures for generating error-correcting output codes in classifier ensembles,
PRL(26), No. 1, 1 January 2005, pp. 83-90.
WWW Link. 0501
BibRef

Kuncheva, L.I.[Ludmila I.],
A Theoretical Study on Six Classifier Fusion Strategies,
PAMI(24), No. 2, February 2002, pp. 281-286.
IEEE DOI 0202
2 classes and L classifiers. Minimum, Maximum, Average, Median, Majority, Product. Minimum/Maximum best. See also Experimental Evaluation of Expert Fusion Strategies. BibRef

Kuncheva, L.I.[Ludmila I.], Whitaker, C.J.,
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy,
MachLearn(51), 2003, pp. 181-207 BibRef 0300

Kuncheva, L.I.[Ludmila I.], Whitaker, C.J., Shipp, C.A., Duin, R.P.W.,
Limits on the Majority Vote Accuracy in Classifier Fusion,
PAA(6), No. 1, 2003, pp. 22-31. BibRef 0300
Earlier:
Is Independence Good for Combining Classifiers?,
ICPR00(Vol II: 168-171).
IEEE DOI 0009
BibRef

Kuncheva, L.I.[Ludmila I.], Whitaker, C.J., Narasimhamurthy, A.,
A case-study on naive labelling for the nearest mean and the linear discriminant classifiers,
PR(41), No. 10, October 2008, pp. 3010-3020.
WWW Link. 0808
Semi-supervised learning; Unlabelled data; On-line classifiers; Naive labelling BibRef

Alexandre, L.A.[Luís A.], Campilho, A.C.[Aurélio C.], Kamel, M.S.[Mohamed S.],
On combining classifiers using sum and product rules,
PRL(22), No. 12, October 2001, pp. 1283-1289.
Elsevier DOI 0108
BibRef
Earlier:
Combining Independent and Unbiased Classifiers Using Weighted Average,
ICPR00(Vol II: 495-498).
IEEE DOI 0009
BibRef

Lu, Y.[Yue], Tan, C.L.[Chew Lim],
Combination of multiple classifiers using probabilistic dictionary and its application to postcode recognition,
PR(35), No. 12, December 2002, pp. 2823-2832.
WWW Link. 0209
BibRef

Oh, S.B.[Sang-Bong],
On the relationship between majority vote accuracy and dependency in multiple classifier systems,
PRL(24), No. 1-3, January 2003, pp. 359-363.
Elsevier DOI 0211
BibRef

Murua, A.[Alejandro],
Upper Bounds for Error Rates of Linear Combinations of Classifiers,
PAMI(24), No. 5, May 2002, pp. 591-602.
IEEE DOI 0205
Analyze classifiers constructed with same training data. Depending on dependence between them, linear combinations will achieve good error performance. BibRef

Giacinto, G.[Giorgio], Roli, F.[Fabio],
An approach to the automatic design of multiple classifier systems,
PRL(22), No. 1, January 2001, pp. 25-33.
Elsevier DOI 0105
See also Combination of neural and statistical algorithms for supervised classification of remote-sensing images. BibRef
Earlier:
A Theoretical Framework for Dynamic Classifier Selection,
ICPR00(Vol II: 8-11).
IEEE DOI 0009
BibRef
Earlier:
Methods for dynamic classifier selection,
CIAP99(659-664).
IEEE DOI 9909
BibRef
Earlier:
Adaptive selection of image classifiers,
CIAP97(I: 38-45).
Springer DOI 9709
BibRef

Fumera, G.[Giorgio], Roli, F.[Fabio], Giacinto, G.[Giorgio],
Reject option with multiple thresholds,
PR(33), No. 12, December 2000, pp. 2099-2101.
WWW Link. 0401
BibRef

Pillai, I.[Ignazio], Fumera, G.[Giorgio], Roli, F.[Fabio],
Threshold optimisation for multi-label classifiers,
PR(46), No. 7, July 2013, pp. 2055-2065.
Elsevier DOI 1303
BibRef
Earlier:
F-measure optimisation in multi-label classifiers,
ICPR12(2424-2427).
WWW Link. 1302
Multi-label classification; S-Cut thresholding; F measure; Precision-recall curve BibRef

Pillai, I.[Ignazio], Fumera, G.[Giorgio], Roli, F.[Fabio],
Multi-label classification with a reject option,
PR(46), No. 8, August 2013, pp. 2256-2266.
Elsevier DOI 1304
BibRef
Earlier:
A Classification Approach with a Reject Option for Multi-label Problems,
CIAP11(I: 98-107).
Springer DOI 1109
Multi-label classification; Manual annotation; Reject option BibRef

Giacinto, G.[Giorgio], Roli, F.[Fabio],
Dynamic classifier selection based on multiple classifier behaviour,
PR(34), No. 9, September 2001, pp. 1879-1881.
WWW Link. 0108
BibRef

Giacinto, G.[Giorgio], Roli, F.[Fabio], Didaci, L.[Luca],
Fusion of multiple classifiers for intrusion detection in computer networks,
PRL(24), No. 12, August 2003, pp. 1795-1803.
WWW Link. 0304
BibRef
Earlier: A1, A2 only:
Intrusion detection in computer networks by multiple classifier systems,
ICPR02(II: 390-393).
IEEE DOI 0211
BibRef

Giacinto, G.[Giorgio], Perdisci, R.[Roberto], Roli, F.[Fabio],
Network Intrusion Detection by Combining One-Class Classifiers,
CIAP05(58-65).
Springer DOI 0509
BibRef

Fumera, G.[Giorgio], Roli, F.[Fabio],
Analysis of error-reject trade-off in linearly combined multiple classifiers,
PR(37), No. 6, June 2004, pp. 1245-1265.
WWW Link. 0405
BibRef

Roli, F., Fumera, G., Vernazza, G.,
Analysis of error-reject trade-off in linearly combined classifiers,
ICPR02(II: 120-123).
IEEE DOI 0211
BibRef
Earlier: A2, A1, A3:
A method for error rejection in multiple classifier systems,
CIAP01(454-458).
WWW Link. 0210
BibRef

Lin, X.F.[Xiao-Fan], Yacoub, S.[Sherif], Burns, J.[John], Simske, S.J.[Steven J.],
Performance analysis of pattern classifier combination by plurality voting,
PRL(24), No. 12, August 2003, pp. 1959-1969.
WWW Link. 0304
BibRef

Fumera, G.[Giorgio], Roli, F.[Fabio],
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems,
PAMI(27), No. 6, June 2005, pp. 942-956.
IEEE Abstract. 0505
Analysis follows on See also Analysis of decision boundaries in linearly combined neural classifiers. Performance depends on individual classifiers and correlation between them. BibRef

Fumera, G.[Giorgio], Fabio, R.[Roli], Alessandra, S.[Serrau],
A Theoretical Analysis of Bagging as a Linear Combination of Classifiers,
PAMI(30), No. 7, July 2008, pp. 1293-1299.
IEEE DOI 0806
Analysis derived from linear combinations to Bagging approaches. BibRef

Demontis, A.[Ambra], Russu, P.[Paolo], Biggio, B.[Battista], Fumera, G.[Giorgio], Roli, F.[Fabio],
On Security and Sparsity of Linear Classifiers for Adversarial Settings,
SSSPR16(322-332).
Springer DOI 1611
BibRef

Biggio, B.[Battista], Fumera, G.[Giorgio], Roli, F.[Fabio],
Adversarial Pattern Classification Using Multiple Classifiers and Randomisation,
SSPR08(500-509).
Springer DOI 0812
BibRef

Didaci, L.[Luca], Giacinto, G.[Giorgio], Roli, F.[Fabio], Marcialis, G.L.[Gian Luca],
A study on the performances of dynamic classifier selection based on local accuracy estimation,
PR(38), No. 11, November 2005, pp. 2188-2191.
WWW Link. 0509
BibRef

Yildiz, O.T.[Olcay Taner], Alpaydin, E.[Ethem],
Ordering and Finding the Best of K>2 Supervised Learning Algorithms,
PAMI(28), No. 3, March 2006, pp. 392-402.
IEEE DOI 0602
Given a dataset and a set of algorithms, find the one that is best. BibRef

Yildiz, O.T.[Olcay Taner],
Quadratic programming for class ordering in rule induction,
PRL(54), No. 1, 2015, pp. 63-68.
Elsevier DOI 1502
Rule induction BibRef

Ata, S.[Sezin], Yildiz, O.T.[Olcay Taner],
Searching for the optimal ordering of classes in rule induction,
ICPR12(1277-1280).
WWW Link. 1302
BibRef

Rodriguez, J.J., Kuncheva, L.I.[Ludmila I.], Alonso, C.J.,
Rotation Forest: A New Classifier Ensemble Method,
PAMI(28), No. 10, October 2006, pp. 1619-1630.
IEEE DOI 0609
BibRef

Rodríguez, J.J.[Juan J.], Kuncheva, L.I.[Ludmila I.],
Combining Online Classification Approaches for Changing Environments,
SSPR08(520-529).
Springer DOI 0812
BibRef

Kuncheva, L.I.[Ludmila I.], Vetrov, D.P.,
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization,
PAMI(28), No. 11, November 2006, pp. 1798-1808.
IEEE DOI 0609
BibRef

Cabrera, J.B.D.[João B.D.],
On the impact of fusion strategies on classification errors for large ensembles of classifiers,
PR(39), No. 11, November 2006, pp. 1963-1978.
WWW Link. 0608
Classifier fusion; Asymptotic methods; Independent classifiers; Sensor networks BibRef

Canuto, A.M.P.[Anne M.P.], Abreu, M.C.C.[Marjory C.C.], de Melo Oliveira, L.[Lucas], Xavier, Jr., J.C.[João C.], de M. Santos, A.[Araken],
Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles,
PRL(28), No. 4, 1 March 2007, pp. 472-486.
WWW Link. 0701
Diversity measures; Classifier ensembles; Selection-based combination methods; Fusion-based combination methods BibRef

Hu, R.[Roland], Damper, R.I.,
A 'No Panacea Theorem' for classifier combination,
PR(41), No. 8, August 2008, pp. 2665-2673.
WWW Link. 0805
BibRef
Earlier:
A 'No Panacea Theorem' for Multiple Classifier Combination,
ICPR06(II: 1250-1253).
IEEE DOI 0609
Probability density functions; Gaussian mixtures; `No Free Lunch' theorems BibRef

Lorena, A.C.[Ana Carolina], de Carvalho, A.C.P.L.F.[André C.P.L.F.], Gama, J.M.P.[João M. P.],
A review on the combination of binary classifiers in multiclass problems,
AIR(30), No. 1-4, December 2008, pp. 19-37.
WWW Link. 1208
BibRef


Freitas, C.O.A.[Cinthia O. A.], de Carvalho, J.M.[João M.], Oliveira, J.J.[José-Josemar], Aires, S.B.K.[Simone B. K.], Sabourin, R.[Robert],
Confusion Matrix Disagreement for Multiple Classifiers,
CIARP07(387-396).
Springer DOI 0711
BibRef

Moreno-Seco, F.[Francisco], Iñesta, J.M.[José M.], Ponce de León, P.J.[Pedro J.], Micó, L.[Luisa],
Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks,
SSPR06(705-713).
Springer DOI 0608
BibRef

Abdul Kader, A.[Ahmad], Drakopoulos, J.A.[John A.], Zhang, Q.[Qi],
Comparative Classifier Aggregation,
ICPR06(III: 156-159).
IEEE DOI 0609
BibRef

Bertolami, R.[Roman], Bunke, H.[Horst],
Early feature stream integration versus decision level combination in a multiple classifier system for text line recognition,
ICPR06(II: 845-848).
IEEE DOI 0609
BibRef

Duin, R.P.W.,
The combining classifier: to train or not to train?,
ICPR02(II: 765-770).
IEEE DOI 0211
BibRef

Schiele, B.,
How many classifiers do I need?,
ICPR02(II: 176-179).
IEEE DOI 0211
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Decision Fusion .


Last update:Sep 25, 2017 at 16:36:46