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.G.,
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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).
IEEE DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
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
Berger, A.[Anna],
Guda, S.[Sergey],
Threshold optimization for F measure of macro-averaged precision and
recall,
PR(102), 2020, pp. 107250.
Elsevier DOI
2003
Macro-averaged F measure, Multi-label classification,
Optimal threshold selection, Fixed point method
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
Optimal Transport .