Dynamic selection of the best base classifier in One versus One

Knowledge-Based Systems

Volumn 85, Issue , 2015, Pages 298-306

  Mendialdua, I ^a   Martinez Otzeta J M ^a   Rodriguez Rodriguez, I ^a   Ruiz Vazquez, T ^a   Sierra, B ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Classifier combination; Decomposition strategies; Dynamic classifier selection; Machine learning; One against One; Supervised classification

Indexed keywords

LEARNING SYSTEMS; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; TEXT PROCESSING;

CLASSIFIER COMBINATION; DECOMPOSITION STRATEGY; DYNAMIC CLASSIFIER SELECTION; ONE AGAINST ONE; SUPERVISED CLASSIFICATION;

SUPERVISED LEARNING;

EID: 84937526497     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.05.015     Document Type: Article

References (39)

1. D.W. Aha, D. Kibler, and M.K. Albert Instance-based learning algorithms Machine Learn. 6 1991 37 66
2. R. Anand, K. Mehrotra, C.K. Mohan, and S. Ranka Efficient classification for multiclass problems using modular neural networks IEEE Trans. Neural Netw. 6 1995 117 124
3. A. Arruti, I. Mendialdua, B. Sierra, E. Lazkano, and E. Jauregi New one versus allone method: Nov@ Expert Syst. Appl. 41 2014 6251 6260
4. K. Bache, M. Lichman, UCI machine learning repository, 2013. < http://archive.ics.uci.edu/ml >.
5. M.A. Bagheri, Q. Gao, and S. Escalera Efficient pairwise classification using local cross off strategy Adv. Artif. Intell. 2012 Springer 25 36
6. M.Á. Bautista, S. Escalera, X. Baró, P. Radeva, J. Vitriá, and O. Pujol Minimal design of error-correcting output codes Pattern Recogn. Lett. 33 2012 693 702
7. P.R. Cavalin, R. Sabourin, and C.Y. Suen Dynamic selection approaches for multiple classifier systems Neural Comput. Appl. 22 2013 673 688
8. W.W. Cohen, Fast effective rule induction, in: Proceedings of the Twelfth International Conference on Machine Learning, 1995, pp. 115-123.
9. T.G. Dietterich, and G. Bakiri Solving multiclass learning problems via error-correcting output codes J. Artif. Intell. Res. 2 1995 263 286
10. E.M. Dos Santos, R. Sabourin, and P. Maupin A dynamic overproduce-and-choose strategy for the selection of classifier ensembles Pattern Recogn. 41 2008 2993 3009
11. B. Fei, and J. Liu Binary tree of svm: a new fast multiclass training and classification algorithm IEEE Trans. Neural Netw. 17 2006 696 704
12. J. Friedman, Another approach to polychotomous classifcation, Technical report, Stanford University, Department of Statistics, 1996.
13. N. Friedman, D. Geiger, and M. Goldszmidt Bayesian network classifiers Machine Learn. 29 1997 131 163
14. J. Fürnkranz Round robin classification J. Machine Learn. Res. 2 2002 721 747
15. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes Pattern Recogn. 44 2011 1761 1776
16. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera Dynamic classifier selection for one-vs-one strategy: avoiding non-competent classifiers Pattern Recogn. 46 2013 3412 3424
17. S. García, A. Fernández, J. Luengo, and F. Herrera Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power Inform. Sci. 180 2010 2044 2064
18. N. García-Pedrajas, and D. Ortiz-Boyer An empirical study of binary classifier fusion methods for multiclass classification Inform. Fusion 12 2011 111 130
19. G. Giacinto, and F. Roli Methods for dynamic classifier selection International Conference on Image Analysis and Processing 1999 IEEE Computer Society 659-659
20. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The weka data mining software: an update ACM SIGKDD Explor. Newslett. 11 2009 10 18
21. T. Hastie, and R. Tibshirani Classification by pairwise coupling Ann. Stat. 26 1998 451 471
22. E. Hüllermeier, and S. Vanderlooy Combining predictions in pairwise classification: an optimal adaptive voting strategy and its relation to weighted voting Pattern Recogn. 43 2010 128 142
23. G.H. John, and P. Langley Estimating continuous distributions in bayesian classifiers Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence 1995 Morgan Kaufman Publishers Inc. 338 345
24. M.N. Kapp, R. Sabourin, and P. Maupin A dynamic model selection strategy for support vector machine classifiers Appl. Soft Comput. 12 2012 2550 2565
25. A.H. Ko, R. Sabourin, and A.S. Britto Jr. From dynamic classifier selection to dynamic ensemble selection Pattern Recogn. 41 2008 1718 1731
26. G. Lebrun, O. Lezoray, C. Charrier, and H. Cardot An ea multi-model selection for svm multiclass schemes Proceedings of the Ninth International Work Conference on Artificial Neural Networks (IWANN07) 2007 Springer 260 267
27. M. Liepert, Topological fields chunking for german with svms: optimizing svm-parameters with gas, in: Proceedings of the International Conference on Recent Advances in Natural Language Processing, 2003.
28. R. Liu, and B. Yuan Multiple classifiers combination by clustering and selection Inform. Fusion 2 2001 163 168
29. A.C. Lorena, A.C. de Carvalho, and J.M. Gama A review on the combination of binary classifiers in multiclass problems Artif. Intell. Rev. 30 2008 19 37
30. A.C. Lorena, and A.C. De Carvalho Evolutionary tuning of svm parameter values in multiclass problems Neurocomputing 71 2008 3326 3334
31. J.C. Platt Fast training of support vector machines using sequential minimal optimization B. Schölkopf, C.J.C. Burges, A.J. Smola, Advances in Kernel Methods 1999 MIT Press 185 208
32. J.C. Platt, N. Cristianini, and J. Shawe-Taylor Large margin dags for multiclass classification Adv Neural Inform. Process. Syst. 12 2000 547 553
33. J.R. Quinlan C4. 5: Programs for Machine Learning vol. 1 1993 Morgan Kaufman
34. S.R. Reid, Model Combination in Multiclass Classification, Ph.D. thesis, University of Colorado, 2010.
35. B. Sierra, E. Lazkano, I. Irigoien, E. Jauregi, and I. Mendialdua K nearest neighbor equality: giving equal chance to all existing classes Inform. Sci. 181 2011 5158 5168
36. P.C. Smits Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection IEEE Trans. Geosci. Remote Sens. 40 2002 801 813
37. B.F. de Souza, A. de Carvalho, R. Calvo, and R.P. Ishii Multiclass svm model selection using particle swarm optimization Proceedings of the Sixth International Conference on Hybrid Intelligent Systems 2006 IEEE 31-31
38. G. Szepannek, B. Bischl, and C. Weihs On the combination of locally optimal pairwise classifiers Eng. Appl. Artif. Intell. 22 2009 79 85
39. K. Woods Combination of multiple classifiers using local accuracy estimates IEEE Trans. Pattern Anal. Machine Intell. 19 1997 405 410