An empirical study of binary classifier fusion methods for multiclass classification

Information Fusion

Volumn 12, Issue 2, 2011, Pages 111-130

  García Pedrajas, Nicolás ^a   Ortiz Boyer, Domingo ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Class binarization; Classification; Multiclass problems; Output coding

Indexed keywords

BASE LEARNERS; BINARIZATIONS; BINARY CLASSIFIERS; CLASSIFICATION; CODING STRATEGY; EMPIRICAL STUDIES; ENSEMBLES OF CLASSIFIERS; FUSION PROCESS; INDIVIDUAL CLASSIFIERS; MULTI-CLASS CLASSIFICATION; MULTI-CLASS CLASSIFIER; MULTI-CLASS PROBLEMS; MULTICLASS CLASSIFICATION PROBLEMS; MULTICLASSIFIER SYSTEM; NEW RESULTS; OUTPUT CODING; REAL-WORLD PROBLEM; TESTING ERRORS; UCI MACHINE LEARNING REPOSITORY;

BEHAVIORAL RESEARCH; CLASSIFIERS; FACE RECOGNITION; INFORMATION FUSION;

LEARNING SYSTEMS;

EID: 78650572282     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2010.06.010     Document Type: Article

References (54)

1. B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: D. Haussler (Ed.), Proceedings of the 5th Annual ACM Workshop on COLT, 1992, pp. 144-152.
2. E.M. Dos Santos, R. Sabourin, and P. Maupin Overfitting cautious selection of classifiers ensembles with genetic algorithms Information Fusion 10 2009 150 162
3. T. Windeatt, and R. Ghaderi Binary labelling and decision-level fusion Information Fusion 2 2001 103 112
4. T. Windeatt Diversity measures for multiple classifier system analysis and design Information Fusion 6 2005 21 36
5. Z. He, X. Xu, and S. Deng A cluster ensemble method for clustering categorical data Information Fusion 6 2005 143 151
6. P. Clark, R. Boswell, Rule induction with CN2: some recent improvements, in: Proceedings of the 5th European Working Session on Learning (EWSL-91), Springer-Verlag, Porto, Portugal, 1991, pp. 151-163.
7. R. Anand, K.G. Mehrotra, C.K. Mohan, and S. Ranka Efficient classification for multiclass problems using modular neural networks IEEE Transactions on Neural Networks 6 1995 117 124
8. S. Knerr, L. Personnaz, and G. Dreyfus Single-layer learning revisited: a stepwise procedure for building and training a neural network J. Fogelman, Neurocomputing: Algorithms Architectures and Applications 1990 Springer-Verlag New York
9. T. Hastie, and R. Tibshirani Classification by pairwise coupling The Annals of Statistics 26 2 1998 451 471
10. J.C. Platt, N. Christiani, J. Shawe-Taylor, Large margin DAGs for multiclass classification, in: S.A. Solla, T.K. Leen, K.-R. Müller (Eds.), Proceedings of Neural Information Processing Systems, NIPS'99, MIT Press, 2000, pp. 547-553.
11. T.-F. Wu, C.-J. Lin, and R.C. Weng Probability estimates for multi-class classification by pairwise coupling Journal of Machine Learning Research 5 2004 975 1005
12. N. García-Pedrajas, and D. Ortiz-Boyer Improving multiclass pattern recognition by the combination of two strategies IEEE Transactions on Pattern Analysis and Machine Intelligence 28 6 2006 1001 1006
13. T.G. Dietterich, and G. Bakiri Solving multiclass learning problems via error-correcting output codes Journal of Artificial Intelligence Research 2 1995 263 286
14. T. Windeatt, and R. Ghaderi Coding and decoding strategies for multi-class learning problems Information Fusion 4 2003 11 21
15. E.B. Kong, T.G. Dietterich, Why error-correcting output coding works with decision trees, Technical Report, Department of Computer Science, Oregon State University, Corvallis, OR, 1995.
16. R. Rifkin, and A. Klautau In Defense of One-Vs-All Classification Journal of Machine Learning Research 5 2004 101 141
17. M. Moreira, E. Mayoraz, Improved pairwise coupling classification with correcting classifiers, in: Proceedings of the 10th European Conference on Machine Learning, vol. 1398, Lecture Notes in Computer Science, Springer, Chemnitz, Germany, 1998, pp. 160-171, doi:10.1007/BFb0026686.
18. E.L. Allwein, R.E. Schapire, and Y. Singer Reducing multiclass to binary: a unifying approach for margin classifiers Journal of Machine Learning Research 1 2000 113 141
19. J. Fürkranz Round robin classification Journal of Machine Learning Research 2 2002 721 747
20. W. Utschick, Error-correcting classification based on neural networks, Ph.D. thesis, Technische Universitat Munchen, 1998.
21. E.B. Kong, T.G. Dietterich, Error-correcting output coding corrects bias and variance, in: A. Prieditis, J.F. Lemmer (Eds.), Machine Learning: Proceedings of the Twelfth International Conference, Elsevier Science Publishers, 1995, pp. 275-283.
22. C.-W. Hsu, and C.-J. Lin A comparison of methods for multiclass support vector machines TNN 13 2 2002 415 425
23. A. Passerini, M. Pontil, and P. Frasconi New results on error correcting output codes of kernel machines IEEE Transactions on Neural Networks 15 1 2004 45 54
24. J.D.M. Rennie, R. Rifkin, Improving multiclass text classification with the support vector machine, Technical Report 2001-026, MIT, 2001.
25. F. Masulli, G. Valentini, Effectiveness of error correcting output codes in multiclass learning problems, in: J. Kittler, F. Roli (Eds.), Multiple Classifier Systems, MCS2000, Lecture Notes in Computer Science, Springer, Cagliary, Italy, 2000, pp. 107-116.
26. F. Masulli, and G. Valentini Effectiveness of error correcting output coding methods in ensemble and monolithic learning machines Patterns Analysis and Applications 6 2003 285 300
27. R.E. Shapire, Using output codes to boost multiclass learning problems, in: D.H. Fisher (Ed.), Proceedings of the 14th International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1997, pp. 313-321.
28. G.M. James, and T. Hastie The error coding method and PICT'S Computational and Graphical Statistics 7 1998 377 387
29. O. Pujol, P. Radeva, and J. Vitriá Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes IEEE Transactions on Pattern Analysis and Machine Intelligence 28 6 2006 1007 1012
30. O. Pujol, S. Escalera, and P. Radeva An incremental node embedding technique for error correcting output codes Pattern Recognition 41 2008 713 725 10.1016/j.patcog.2007.04.008
31. S. Escalera, D.M.J. Tax, O. Pujol, P. Radeva, and R.P.W. Duin Subclass problem-dependent design for error-correcting output codes IEEE Transactions on Pattern Analysis and Machine Intelligence 30 6 2008 1041 1054 10.1109/TPAMI.2008.38
32. I. Melvin, E. Ie, J. Weston, W.S. Noble, and C. Leslie Multi-class protein classification using adaptive codes Journal of Machine Learning Research 8 2007 1557 1581
33. T.G. Dietterich Approximate statistical tests for comparing supervised classification learning algorithms Neural Computation 10 7 1998 1895 1923
34. F. Wilcoxon Individual comparisons by ranking methods Biometrics 1 1945 80 83
35. J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30 (Pubitemid 43022939)
36. M. Friedman A comparison of alternative tests of significance for the problem of m rankings Annals of Mathematicals Statistics 11 1940 86 92
37. R.L. Iman, and J.M. Davenport Approximations of the critical regions of the Friedman statistic Communications in Statistics 6 1980 571 595
38. Y. Yang, G.I. Webb, A comparative study of discretization methods for Naive-Bayes classifiers, in: Proceedings of the 2002 Pacific Rim Knowledge Acquisition Workshop (PKAW 2002), Tokyo, Japan, 2002, pp. 159-173.
39. U.M. Fayyad, K.B. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceedings of the 13th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, Chambéry, France, 1993, pp. 1022-1029.
40. C.-C. Chang, C.-J. Lin, LIBSVM: a library for support vector machines. < http://www.csie.ntu.edu.tw/cjlin/libsvm >, 2001.
41. R.C. Bose, and D.K. Ray-Chaudhuri On a class of error-correcting binary group codes Information and Control 3 1960 68 79
42. L.I. Kuncheva Using diversity measures for generating error-correcting output codes Pattern Recognition Letters 26 2005 83 90
43. N. Hatami, S. Seyedtabaii, Error correcting output codes using genetic algorithm-based decoding, in: Proceedings of the Fourth International Conference on Networked Computing and Advanced Information Management, vol. 1, IEEE Computer Society, Los Alamitos, CA, USA, 2008, pp. 391-396, doi:10.1109/NCM. 2008.260.
44. D. Ortiz-Boyer, C. Hervás-Martínez, and N. García-Pedrajas CIXL2: A crossover operator for evolutionary algorithms based on population features Journal of Artificial Intelligence Research 24 2005 33 80
45. L.J. Eshelman The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination 1990 Morgan Kauffman San Mateo, CA
46. D.H. Wolpert, and W.G. Macready No Free Lunch Theorems for Optimization IEEE Transactions on Evolutionary Computation 1 1 1997 67 82
47. E. Alpaydin Combined 5 × 2cv F test for comparing supervised classification learning algorithms Neural Computation 11 1999 1885 1892
48. N. García-Pedrajas, and C. Fyfe Evolving output codes for multiclass problems IEEE Transactions of Evolutionary Computation 12 1 2007 93 106
49. G. Yule On the association of attributes in statistics Philosophical Transactions of the Royal Society of London 194 1900 257 319
50. L. Kuncheva, and C.J. Whitaker Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy Machine Learning 51 2 2003 181 207
51. F. Masulli, G. Valentini, Quantitative evaluation of dependence among outputs in ECOC classifiers using mutual information based measures, in: K. Marko, P. Werbos (Eds.), Proceedings of the International Joint Conference on Neural Networks IJCNN'01, vol. 2, IEEE, Piscataway, NJ, USA, 2001, pp. 784-789.
52. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufman San Mateo
53. K. Crammer, and Y. Singer On the algorithmic implementation of multi-class SVMs Journal of Machine Learning Research 2 2001 265 292
54. T.G. Dietterich An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization Machine Learning 40 2000 139 157