14.1.5.2 Multiple Classifiers, Combining Classifiers, Combinations

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 Version. 0309
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 Version. 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 Version. 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 Version. 0309
BibRef

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

Skurichina, M., Duin, R.P.W.,
Bagging for Linear Classifiers,
PR(31), No. 7, July 1998, pp. 909-930.
WWW Version. 9807
BibRef
Earlier:
Stabilizing Classifiers for Very Small Sample Sizes,
ICPR96(II: 891-896).
IEEE DOI Link 9608
(TU Delft, NL) 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 Version. 0011
BibRef

Windeatt, T.[Terry],
Vote counting measures for ensemble classifiers,
PR(36), No. 12, December 2003, pp. 2743-2756.
WWW Version. 0310
BibRef

Windeatt, T.,
Diversity/accuracy and ensemble classifier design,
ICPR04(III: 454-457).
IEEE DOI Link 0409
BibRef

Windeatt, T., Ardeshir, G.,
Tree pruning for output coded ensembles,
ICPR02(II: 92-95).
IEEE DOI Link 0211
Convert a multi-class problem into several binary subproblems with an ensemble of binary classifiers. 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 Link 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 Abstract.
WWW Version. 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 Abstract.
WWW Version. 9705
BibRef
Earlier: CVPR96(391-396).
IEEE Abstract.
WWW Version. 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

Shen, Q.A.[Qi-Ang], Chouchoulas, A.[Alexios],
Combining rough sets and data-driven fuzzy learning for generation of classification rules,
PR(32), No. 12, December 1999, pp. 2073-2076.
WWW Version. BibRef 9912

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 Link 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 Abstract.
WWW Version. 0003
Linearly combined. Minimum classifier error used to estimate weights. BibRef

Dietterich, T.G.,
Random Forests,
MachLearn(40), No. 2, 2000, pp. 139-157. BibRef 0001

Kleinberg, E.M.[Eugene M.],
On the Algorithmic Implementation of Stochastic Discrimination,
PAMI(22), No. 5, May 2000, pp. 473-490.
IEEE Abstract.
WWW Version. 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 Version. 0101
BibRef
Earlier:
A Unified View of Rank-based Decision Combination,
ICPR00(Vol II: 479-482).
IEEE DOI Link 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 Version. 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 Version. 0304
BibRef
Earlier:
Why does output normalization create problems in multiple classifier systems?,
ICPR02(II: 775-778).
IEEE DOI Link 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 Link 0105
BibRef
Earlier: A2, A1:
On the Relationship between Neural Networks and Fuzzy Reasoning,
ICPR96(IV: 239-243).
IEEE DOI Link 9608
(Univ. of Surrey, UK) 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 Link 0105
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 Link 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 Link 0108
BibRef

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

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 Link 0711
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 Version. 0208
BibRef

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

Hothorn, T.[Torsten], Lausen, B.[Berthold],
Double-bagging: combining classifiers by bootstrap aggregation,
PR(36), No. 6, June 2003, pp. 1303-1309.
WWW Version. 0304
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.
HTML Version. 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.).
HTML Version. 0303
BibRef

Impedovo, D.[Donato], Pirlo, G.[Giuseppe],
Generating Sets of Classifiers for the Evaluation of Multi-expert Systems,
ICPR10(2166-2169).
IEEE DOI Link 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.).
HTML Version. 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 Link 9909
BibRef

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

Impedovo, D., Pirlo, G., Sarcinella, L., Stasolla, E.,
Artificial Classifier Generation for Multi-expert System Evaluation,
FHR10(421-426).
IEEE DOI Link 1011
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 Link 0907
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

He, C.[Chao], Girolami, M.A.[Mark A.], Ross, G.[Gary],
Employing optimized combinations of one-class classifiers for automated currency validation,
PR(37), No. 6, June 2004, pp. 1085-1096.
WWW Version. 0405
BibRef

Xing, D.S.[Dong-Shan], Girolami, M.A.[Mark A.],
Employing Latent Dirichlet Allocation for fraud detection in telecommunications,
PRL(28), No. 13, 1 October 2007, pp. 1727-1734.
WWW Version. 0709
Fraud detection; Telecommunications; User modelling; Data mining; Latent Dirichlet Allocation 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 Version. 0509
BibRef
Earlier: CVPR04(I: 255-260).
IEEE Abstract. 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

Shin, H.W., Sohn, S.Y.,
Selected tree classifier combination based on both accuracy and error diversity,
PR(38), No. 2, February 2005, pp. 191-197.
WWW Version. 0411
Build tree classifier, cluster them. BibRef

Sohn, S.Y., Shin, H.W.,
Experimental study for the comparison of classifier combination methods,
PR(40), No. 1, January 2007, pp. 33-40.
WWW Version. 0611
Bagging; Random subspace method; Classifier selection; Parametric fusion 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 Version. 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 Version. 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 Version. 0509
BibRef
Earlier:
Feasible Adaptation Criteria for Hybrid Wavelet: Large Margin Classifiers,
CAIP03(588-595).
WWW Version. 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 Version. 0509
BibRef
And:
Experimental Comparison of Combination Rules using Simulated Data,
ICPR06(III: 152-155).
WWW Version. 0609
BibRef

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

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

Alpaydin, E.[Ethem],
Multiple neural networks and weighted voting,
ICPR92(II:29-32).
IEEE DOI Link 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 Link 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 Link 0409
BibRef

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

Nanni, L.[Loris], Lumini, A.[Alessandra],
FuzzyBagging: A novel ensemble of classifiers,
PR(39), No. 3, March 2006, pp. 488-490.
WWW Version. 0601
BibRef

Nanni, L.[Loris], Lumini, A.[Alessandra],
Ensemblator: An ensemble of classifiers for reliable classification of biological data,
PRL(28), No. 5, 1 April 2007, pp. 622-630.
WWW Version. 0703
Ensemble of classifiers; Machine learning; Bioinformatics 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 Version. 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 Link 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 Version. 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 Link
WWW Version. 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 Version. 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 Version. 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 Version. 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 Link
WWW Version. 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 Link
WWW Version. 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 Version. 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 Version. 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 Version. 0704
Ensemble learning; Meta learning; Regression BibRef

Ko, A.H.R.[Albert Hung-Ren], Sabourin, Jr., R.[Robert], de Souza Britto, A.[Alceu], Soares de Oliveira, L.E.[Luiz E.],
Pairwise fusion matrix for combining classifiers,
PR(40), No. 8, August 2007, pp. 2198-2210.
WWW Version. 0704
BibRef
Earlier: A1, A2, A3, Only:
A New Objective Function for Ensemble Selection in Random Subspaces,
ICPR06(IV: 185-188).
WWW Version. 0609
Fusion function; Combining classifiers; Confusion matrix; Pattern recognition; Majority voting; Ensemble of learning machines BibRef

Ko, A.H.R.[Albert Hung-Ren], Sabourin, Jr., R.[Robert], de Souza Britto, A.[Alceu],
From dynamic classifier selection to dynamic ensemble selection,
PR(41), No. 5, May 2008, pp. 1735-1748.
WWW Version. 0711
BibRef
Earlier:
K-Nearest Oracle for Dynamic Ensemble Selection,
ICDAR07(422-426).
IEEE DOI Link 0709
Oracle; Combining classifiers; Classifier selection; Ensemble selection; Pattern recognition; Majority voting; Ensemble of learning machines BibRef

Ko, A.H.R.[Albert Hung-Ren], Sabourin, Jr., R.[Robert], Soares de Oliveira, L.E.[Luiz E.], de Souza Britto, A.[Alceu],
The implication of data diversity for a classifier-free ensemble selection in random subspaces,
ICPR08(1-5).
IEEE DOI Link 0812
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 Version. 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 Link 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 Version. 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 Link 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.
WWW Version. 0510
Apply to ATR task. BibRef

Zhu, X.Q.[Xing-Quan], Yang, Y.[Ying],
A lazy bagging approach to classification,
PR(41), No. 10, October 2008, pp. 2980-2992.
WWW Version. 0808
Classification; Classifier ensemble; Bagging; Lazy learning BibRef

dos Santos, E.M.[Eulanda M.], Sabourin, R.[Robert], Maupin, P.[Patrick],
A dynamic overproduce-and-choose strategy for the selection of classifier ensembles,
PR(41), No. 10, October 2008, pp. 2993-3009.
WWW Version. 0808
Overproduce-and-choose strategy; Dynamic classifier selection; Optimization; Measures of confidence 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 Version. 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 Version. 0804
Cluster ensembles; Consensus clustering; Distributed clustering; Adaptive clustering BibRef

Carneiro, G.[Gustavo], Vasconcelos, N.[Nuno],
Minimum Bayes error features for visual recognition,
IVC(27), No. 1-2, January 2009, pp. 131-140.
WWW Version. 0811
BibRef
Earlier:
Minimum Bayes Error Features for Visual Recognition by Sequential Feature Selection and Extraction,
CRV05(253-260).
IEEE DOI Link 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 Link 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 Link 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 Link
WWW Version. 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 Link
WWW Version. 0902
BibRef
Earlier:
Spectral aggregation for clustering ensemble,
ICPR08(1-4).
IEEE DOI Link 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 Link 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 Link 0907
BibRef

Hullermeier, E.[Eyke], Vanderlooy, S.[Stijn],
Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting,
PR(43), No. 1, January 2010, pp. 128-142,.
Elsevier DOI Link
WWW Version. 0909
Learning by pairwise comparison; Label ranking; Aggregation strategies; Classifier combination; Weighted voting; MAP prediction BibRef

Martinez-Munoz, G.[Gonzalo], Suarez, A.[Alberto],
Out-of-bag estimation of the optimal sample size in bagging,
PR(43), No. 1, January 2010, pp. 143-152,.
Elsevier DOI Link
WWW Version. 0909
Bagging; Subagging; Bootstrap sampling; Subsampling; Optimal sampling ratio; Ensembles of classifiers; Decision trees BibRef

Tzeng, Y.C.[Yu-Chang], Fan, K.T.[Kou-Tai], Chen, K.S.[Kun-Shan],
An Adaptive Thresholding Multiple Classifiers System for Remote Sensing Image Classification,
PhEngRS(75), No. 6, June 2009, pp. 679-688.
WWW Version. 0910
Bagging and/or Boosting Weighted Multiple Classifiers Systems with an Adaptive Thresholding for Remote Sensing Image Classification 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 Version. 0910
A novel framework for integrating classifiers of variable complexity as applied to specific portions of the input space. BibRef

Sun, S.L.[Shi-Liang],
Local within-class accuracies for weighting individual outputs in multiple classifier systems,
PRL(31), No. 2, 15 January 2010, pp. 119-124.
Elsevier DOI Link
WWW Version. 1001
Distance metric learning; Local within-class accuracy; Majority voting; Multiple classifier system; Random subspace 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 Link
WWW Version. 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 Link 0812
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 Link
WWW Version. 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 Link 0911
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 Link
WWW Version. 1006
BibRef
Earlier:
Weighted Cluster Ensemble Using a Kernel Consensus Function,
CIARP08(195-202).
Springer DOI Link 0809
Cluster ensemble; Kernel function; Similarity measure; Clustering validity index; Consensus partition 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 Link
WWW Version. 1006
BibRef
And:
Boolean Combination of Classifiers in the ROC Space,
ICPR10(4299-4303).
IEEE DOI Link 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 Link
WWW Version. 1109
Classification; Multi-classifier systems; Incremental learning; Adaptive systems; ROC; Information fusion; Hidden Markov models; Anomaly detection; Computer and network security 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 Link
WWW Version. 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 Link
WWW Version. 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 Link
WWW Version. 1003
Clustering ensembles; Weak partitions; ANMI criterion; Binary morphology BibRef

Wang, L.[Liang], Leckie, C.[Christopher], Kotagiri, R.[Ramamohanarao], Bezdek, J.[James],
Approximate pairwise clustering for large data sets via sampling plus extension,
PR(44), No. 2, February 2011, pp. 222-235.
Elsevier DOI Link
WWW Version. 1011
Pairwise data; Selective sampling; Spectral clustering; Graph embedding; Out-of-sample extension BibRef

Wang, L.[Liang], Leckie, C.[Christopher], Kotagiri, R.[Ramamohanarao],
Combining Real and Virtual Graphs to Enhance Data Clustering,
ICPR10(790-793).
IEEE DOI Link 1008
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 Link
WWW Version. 1106
HyCARCE; Data clustering; Hyperellipsoidal clustering; Wireless sensor networks; Low computational cost clustering algorithm BibRef

Rokach, L.[Lior],
Pattern Classification Using Ensemble Methods,
World ScientificSingapore, 2009 ISBN: 978-981-4271-06-6
HTML Version. Survay, Pattern Classification. To purchase this book look here 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 Link
WWW Version. 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 Link
WWW Version. 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 Link 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 Link
WWW Version. 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 Link
WWW Version. 1101
Cluster validation; Cluster validity index; Unsupervised learning 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 Link
WWW Version. 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 Link
WWW Version. 1103
Classification ensembles; Classification trees; Bayesian inference; Infinite ensembles BibRef

Galar, M.[Mikel], Fernandez, A.[Alberto], Barrenechea, E.[Edurne], Bustince, H.[Humberto], Herrera, F.[Francisco],
An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes,
PR(44), No. 8, August 2011, pp. 1761-1776.
Elsevier DOI Link
WWW Version. 1104
Survey, Ensemble Clustering. Multi-classification; Pairwise learning; One-vs-one; One-vs-all; Decomposition strategies; Ensembles BibRef

Jia, J.H.[Jian-Hua], Xiao, X.[Xuan], Liu, B.[Bingxiang], Jiao, L.C.[Li-Cheng],
Bagging-based spectral clustering ensemble selection,
PRL(32), No. 10, 15 July 2011, pp. 1456-1467.
Elsevier DOI Link
WWW Version. 1106
Spectral clustering; Selective clustering ensembles; Bagging; Normalized mutual information (NMI); Adjusted rand index (ARI) BibRef

Irle, A.[Albrecht], Kauschke, J.[Jonas],
On Kleinberg's Stochastic Discrimination Procedure,
PAMI(33), No. 7, July 2011, pp. 1482-1486.
IEEE DOI Link 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 Link
WWW Version. 1101
BibRef
Earlier:
AUC-based Combination of Dichotomizers: Is Whole Maximization also Effective for Partial Maximization?,
ICPR10(73-76).
IEEE DOI Link 1008
Combination of classifiers; ROC analysis; Partial AUC BibRef

Murino, L., Angelini, C., de Feis, I., Raiconi, G., Tagliaferri, R.,
Beyond classical consensus clustering: The least squares approach to multiple solutions,
PRL(32), No. 12, 1 September 2011, pp. 1604-1612.
Elsevier DOI Link
WWW Version. 1108
Clustering; Least-squares consensus; Data visualization 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 Link 1110
BibRef

Geng, B.[Bo], Tao, D.C.[Da-Cheng], Xu, C.[Chao],
DAML: Domain Adaptation Metric Learning,
IP(20), No. 10, October 2011, pp. 2980-2989.
IEEE DOI Link 1110
BibRef

Geng, B.[Bo], Xu, C.[Chao], Tao, D.C.[Da-Cheng], Yang, L.J.[Lin-Jun], Hua, X.S.[Xian-Sheng],
Ensemble manifold regularization,
CVPR09(2396-2402).
IEEE DOI Link 0906
approximate the intrinsic manifold by combining several initial guesses 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 Link 1110
Also use similarity between clusters in consensus. BibRef

Madjarov, G.[Gjorgji], Gjorgjevikj, D.[Dejan], Džeroski, S.[Sašo],
Two stage architecture for multi-label learning,
PR(45), No. 3, March 2012, pp. 1019-1034.
Elsevier DOI Link
WWW Version. 1111
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And:
Dual Layer Voting Method for Efficient Multi-label Classification,
IbPRIA11(232-239).
Springer DOI Link 1106
Multi-label learning; Multi-label ranking; Multi-label classification; Two stage architecture; Classifier chain 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 Link
WWW Version. 1112
Clustering ensemble; Comparing clusterings; Simulated annealing BibRef

Tian, J.[Jin], Li, M.Q.A.[Min-Qi-Ang], 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 Link
WWW Version. 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 Link
WWW Version. 1112
Data mining; Supervised learning; Concept learning; Associative classifier BibRef

Pang, Y.W.[Yan-Wei], Ma, Z.[Zhao], Yuan, Y.[Yuan], Li, X.L.[Xue-Long], Wang, K.Q.[Kong-Qiao],
Multimodal learning for multi-label image classification,
ICIP11(1797-1800).
IEEE DOI Link 1201
BibRef

Ponti, M.P.[Moacir P.], Papa, J.P.[João Paulo], Levada, A.L.M.[Alexandre L. M.],
A Markov Random Field Model for Combining Optimum-Path Forest Classifiers Using Decision Graphs and Game Strategy Approach,
CIARP11(581-590).
Springer DOI Link 1111
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 Link 1111
BibRef

Rajasekhara, P., Pujari, A.K.[Arun K.],
A new clusterwise similarity for partitions based on quantitative disagreement,
ICCVGIP10(117-123).
WWW Version. 1111
Get consnesus clustering. 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 Link 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 Link 1011
Classifier Molding. Imitate arbirtary classifiers by linear regression trees. BibRef

Xue, X.Y.[Xiang-Yang], Luo, H.Z.[Hang-Zai], Fan, J.P.[Jian-Ping],
Structured max-margin learning for multi-label image annotation,
CIVR10(82-88).
WWW Version. 1007
Multiple classifiers. BibRef

Vega-Pons, S.[Sandro], Ruiz-Shulcloper, J.[José],
Partition Selection Approach for Hierarchical Clustering Based on Clustering Ensemble,
CIARP10(525-532).
Springer DOI Link 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 Link 1011
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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 Link 1008
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Armano, G.[Giuliano], Hatami, N.[Nima],
Random Prototype-based Oracle for Selection-fusion Ensembles,
ICPR10(77-80).
IEEE DOI Link 1008
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Zhang, S.H.[Shao-Hong], Wong, H.S.[Hau-San],
ARImp: A Generalized Adjusted Rand Index for Cluster Ensembles,
ICPR10(778-781).
IEEE DOI Link 1008
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Mirzaei, H.[Hamidreza],
A Novel Multi-view Agglomerative Clustering Algorithm Based on Ensemble of Partitions on Different Views,
ICPR10(1007-1010).
IEEE DOI Link 1008
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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 Link 1008
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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 Link 1008
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Erdogan, H.[Hakan], Sen, M.U.[Mehmet Umut],
A Unifying Framework for Learning the Linear Combiners for Classifier Ensembles,
ICPR10(2985-2988).
IEEE DOI Link 1008
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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 Link 1006
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Siddiquie, B.[Behjat], Vitaladevuni, S.N.[Shiv N.], Davis, L.S.[Larry S.],
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WACV09(1-8).
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Multiple features for recognition, combine classifications. BibRef

Takahashi, T.[Tetsuji], Kudo, M.[Mineichi], Nakamura, A.[Atsuyoshi],
Classifier Selection in a Family of Polyhedron Classifiers,
CIARP09(441-448).
Springer DOI Link 0911
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Duin, R.P.W.[Robert P. W.], Pekkalska, E.[Elzbieta],
The Dissimilarity Representation for Structural Pattern Recognition,
CIARP11(1-24).
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Kim, S.W.[Sang-Woon], Duin, R.P.W.[Robert P.W.],
Dissimilarity-Based Classifications in Eigenspaces,
CIARP11(425-432).
Springer DOI Link 1111
BibRef
Earlier:
On Improving Dissimilarity-Based Classifications Using a Statistical Similarity Measure,
CIARP10(418-425).
Springer DOI Link 1011
BibRef
Earlier:
A Combine-Correct-Combine Scheme for Optimizing Dissimilarity-Based Classifiers,
CIARP09(425-432).
Springer DOI Link 0911
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Liu, Y.[Yu], Xu, X.J.[Xiu-Juan], Wang, C.[Chunyu],
Simple Ensemble of Extreme Learning Machine,
CISP09(1-5).
IEEE DOI Link 0910
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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).
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Ranganathan, A.[Ananth],
Semantic Scene Segmentation using Random Multinomial Logit,
BMVC09(xx-yy).
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Rejection Strategies with Multiple Classifiers for Handwritten Character Recognition,
ICDAR09(1126-1130).
IEEE DOI Link 0907
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Parakhin, M.[Mikhail], Haluptzok, P.[Patrick],
Finding the Most Probable Ranking of Objects with Probabilistic Pairwise Preferences,
ICDAR09(616-620).
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Du, P., Sun, H., Zhang, W.,
Multiple classifier combination for target identification from high resolution remote sensing image,
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Sun, Y.[Ying], Qi, H.R.[Hai-Rong],
Dynamic target classification in wireless sensor networks,
ICPR08(1-4).
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Gupta, U.[Upavan], Ranganathan, N.[Nagarajan],
A microeconomic approach to multi-objective spatial clustering,
ICPR08(1-4).
IEEE DOI Link 0812
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Uchida, S.[Seiichi], Amamoto, K.[Kazuma],
Early recognition of sequential patterns by classifier combination,
ICPR08(1-4).
IEEE DOI Link 0812
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Kudo, M.[Mineichi], Nakamura, A.[Atsuyoshi], Takigawa, I.[Ichigaku],
Classification by reflective convex hulls,
ICPR08(1-4).
IEEE DOI Link 0812
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Shidara, Y.[Yohji], Kudo, M.[Mineichi], Nakamura, A.[Atsuyoshi],
Classification by bagged consistent itemset rules,
ICPR08(1-4).
IEEE DOI Link 0812
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Shirai, S.[Satoshi], Kudo, M.[Mineichi], Nakamura, A.[Atsuyoshi],
Bagging, Random Subspace Method and Biding,
SSPR08(801-810).
Springer DOI Link 0812
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Su, X.Y.[Xiao-Yuan], Khoshgoftarr, T.M.[Taghi M.], Zhu, X.Q.[Xing-Quan],
VoB predictors: Voting on bagging classifications,
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Lobrano, C.[Carlo], Tronci, R.[Roberto], Giacinto, G.[Giorgio], Roli, F.[Fabio],
A Score Decidability Index for Dynamic Score Combination,
ICPR10(69-72).
IEEE DOI Link 1008
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Tronci, R.[Roberto], Giacinto, G.[Giorgio], Roli, F.[Fabio],
Dynamic score combination of binary experts,
ICPR08(1-4).
IEEE DOI Link 0812
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Combination of Experts by Classifiers in Similarity Score Spaces,
SSPR08(821-830).
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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).
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Zhu, X.Q.[Xing-Quan], Bao, C.Y.[Cheng-Yi], Qiu, W.D.[Wei-Dong],
Bagging very weak learners with lazy local learning,
ICPR08(1-4).
IEEE DOI Link 0812
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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 Link 0812
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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 Link 0810
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Socorro, R.[Raisa], Micó, L.[Luisa],
Use of Structured Pattern Representations for Combining Classifiers,
SSPR08(811-820).
Springer DOI Link 0812
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Orrite, C.[Carlos], Rodríguez, M.[Mario], Martínez, F.[Francisco], Fairhurst, M.[Michael],
Classifier Ensemble Generation for the Majority Vote Rule,
CIARP08(340-347).
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Ozay, M.[Mete], Vural, F.T.Y.[Fatos Tunay Yarman],
On the Performance of Stacked Generalization Classifiers,
ICIAR08(xx-yy).
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Maiti, C.[Chinmay], Pal, S.[Somnath],
Efficient Multi-method Rule Learning for Pattern Classification Machine Learning and Data Mining,
PReMI07(324-331).
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Kankanala, L.[Laxmi], Murty, M.N.[M. Narasimha],
Hybrid Approaches for Clustering,
PReMI07(25-32).
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Ñanculef, R.[Ricardo], Valle, C.[Carlos], Allende, H.[Héctor], Moraga, C.[Claudio],
Bagging with Asymmetric Costs for Misclassified and Correctly Classified Examples,
CIARP07(694-703).
Springer DOI Link 0711
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Kyrgyzov, I.O.[Ivan O.], Maitre, H.[Henri], Campedel, M.[Marine],
A Method of Clustering Combination Applied to Satellite Image Analysis,
CIAP07(81-86).
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Molinara, M., Ricamato, M.T., Tortorella, F.,
Facing Imbalanced Classes through Aggregation of Classifiers,
CIAP07(43-48).
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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 Link 0706
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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 Link 0706
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Valdovinos, R.M., Sánchez, J.S.,
Performance Analysis of Classifier Ensembles: Neural Networks Versus Nearest Neighbor Rule,
IbPRIA07(I: 105-112).
Springer DOI Link 0706
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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,
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Product Approximation by Minimizing the Upper Bound of Bayes Error Rate for Bayesian Combination of Classifiers,
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Classifier Combination through Clustering in the Output Spaces,
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A multiclass classification method based on multiple pairwise classifiers,
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An adaptive weighted majority vote rule for combining multiple classifiers,
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Optimisation of Multiple Classifier Systems Using Genetic Algorithms,
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Earlier:
Data Description in Subspaces,
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IEEE DOI Link 0009
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Skurichina, M., Ypma, A., Duin, R.P.W.,
The Role of Subclasses in Machine Diagnostics,
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A combination method of two classifiers based on the information of confusion matrix,
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Bagging Is a Small-Data-Set Phenomenon,
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An Optimum Class-Rejective Decision Rule and Its Evaluation,
ICPR10(3312-3315).
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A Family of Cluster Validity Indexes Based on a l-Order Fuzzy OR Operator,
SSPR08(612-621).
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And:
A class-selective rejection scheme based on blockwise similarity of typicality degrees,
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Mascarilla, L., Frélicot, C.,
Another Look at Combining Rejection-based Pattern Classifiers,
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ICDAR97(870-874).
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Static and Dynamic Classifier Fusion for Character Recognition,
ICDAR97(499-506).
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Object recognition: the utopian method is dead; the time for combining simple methods has come,
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Hierarchical Combination, Multi-Stage Classifiers .