14.1.13.2.1 Fusion for Multiple Classifiers

Chapter Contents (Back)
Fusion. Classifier Combinations. Classifier Ensembles.

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.
Elsevier DOI 0011
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

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

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.
Elsevier DOI 0606
Decision fusion; Co-operative training; Combining architecture 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

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.
Elsevier DOI 0808
Receiver operating characteristics; Biometrics; Decision fusion; Machine learning; Pattern classification 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

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

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

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

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

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

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

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

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

Zhang, C., Sargent, I., Pan, X., Gardiner, A., Hare, J., Atkinson, P.M.,
VPRS-Based Regional Decision Fusion of CNN and MRF Classifications for Very Fine Resolution Remotely Sensed Images,
GeoRS(56), No. 8, August 2018, pp. 4507-4521.
IEEE DOI 1808
computer vision, convolution, feature extraction, geophysical image processing, image classification, uncertainty BibRef

Du, X.X.[Xiao-Xiao], Zare, A.[Alina],
Multiple Instance Choquet Integral Classifier Fusion and Regression for Remote Sensing Applications,
GeoRS(57), No. 5, May 2019, pp. 2741-2753.
IEEE DOI 1905
crops, geophysical image processing, image classification, image fusion, learning (artificial intelligence), target detection BibRef

Xu, J.[Jie], Wang, W.[Wei], Wang, H.Y.[Han-Yuan], Guo, J.H.[Jin-Hong],
Multi-model ensemble with rich spatial information for object detection,
PR(99), 2020, pp. 107098.
Elsevier DOI 1912
Ensemble learning, Object detection, Dilated convolution, Feature fusion BibRef

Huang, A.P.[Ai-Ping], Zhao, T.S.[Tie-Song], Lin, C.W.[Chia-Wen],
Multi-View Data Fusion Oriented Clustering via Nuclear Norm Minimization,
IP(29), 2020, pp. 9600-9613.
IEEE DOI 2011
Data integration, Minimization, Matrix decomposition, Optimization, Task analysis, Clustering methods, Feature extraction, nuclear norm BibRef

Pournemat, A.[Ali], Adibi, P.[Peyman], Chanussot, J.[Jocelyn],
Semisupervised charting for spectral multimodal manifold learning and alignment,
PR(111), 2021, pp. 107645.
Elsevier DOI 2012
Semi-supervised learning, Multimodal data, Functional map, Manifold learning, Data fusion, Hyperspectral images BibRef


Ge, W., Yang, S., Yu, Y.,
Multi-evidence Filtering and Fusion for Multi-label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning,
CVPR18(1277-1286)
IEEE DOI 1812
Training, Heating systems, Object detection, Semantics, Image segmentation, Pipelines, Labeling 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

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

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

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

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

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

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

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


Last update:Jul 28, 2021 at 22:23:09