Empowering difficult classes with a similarity-based aggregation in multi-class classification problems

Information Sciences

Volumn 264, Issue , 2014, Pages 135-157

  Galar, Mikel ^a   Fernández, Alberto ^b   Barrenechea, Edurne ^a   Herrera, Francisco ^c,d  

^a PUBLIC UNIVERSITY OF NAVARRE   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

^c UNIVERSITY OF GRANADA   (Spain)

^d KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Decomposition strategies; Difficult classes; Multi class classification; One vs One; Pairwise learning; Tuning

Indexed keywords

EID: 84894490812     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.12.053     Document Type: Article

References (65)

1. D.W. Aha, D. Kibler, and M.K. Albert Instance-based learning algorithms Mach. Learn. 6 1991 37 66
2. J. Alcalá-Fdez, A. Fernández, J. Luengo, J. Derrac, S. García, L. Sánchez, and F. Herrera KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework J. Multiple-Valued Logic Soft Comput. 17 2011 255 287
3. J. Alcalá-Fdez, L. Sánchez, S. García, M.J. del Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J. Fernández, and F. Herrera KEEL: a software tool to assess evolutionary algorithms for data mining problems Soft Comput. 13 3 2009 307 318
4. E.L. Allwein, R.E. Schapire, and Y. Singer Reducing multiclass to binary: a unifying approach for margin classifiers J. Mach. Learn. Res. 1 2000 113 141
5. A. Asuncion, D.J. Newman, UCI Machine Learning Repository, 2007. < http://www.ics.uci.edu/∼mlearn/MLRepository.html >.
6. R. Barandela, J.S. Sánchez, V. García, and E. Rangel Strategies for learning in class imbalance problems Pattern Recogn. 36 3 2003 849 851
7. M. Basu, and T.K. Ho Data Complexity in Pattern Recognition 2006 Springer
8. G. Beliakov, A. Pradera, and T. Calvo Aggregation Functions: A Guide for Practitioners first ed. 2008 Springer Publishing Company, Incorporated
9. S. Bengio, J. Weston, D. Grangier, Label embedding trees for large multi-class tasks, in: NIPS, 2010.
10. H. Bustince, E. Barrenechea, and M. Pagola Restricted equivalence functions Fuzzy Sets Syst. 157 17 2006 2333 2346
11. H. Bustince, E. Barrenechea, and M. Pagola Image thresholding using restricted equivalence functions and maximizing the measures of similarity Fuzzy Sets Syst. 158 5 2007 496 516
12. H. Bustince, E. Barrenechea, and M. Pagola Relationship between restricted dissimilarity functions, restricted equivalence functions and normal en-functions: image thresholding invariant Pattern Recogn. Lett. 29 4 2008 525 536
13. J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
14. T.G. Dietterich, and G. Bakiri Solving multiclass learning problems via error-correcting output codes J. Artif. Intell. Res. 2 1995 263 286
15. S.A. Dudani The distance-weighted k-nearest-neighbor rule IEEE Trans. Syst., Man, Cybernet. 6 4 1976 325 327
16. L.J. Eshelman, and J.D. Schaffer Real-coded genetic algorithms and interval-schemata D.L. Whitley, Foundation of Genetic Algorithms 2 1993 Morgan Kaufmann San Mateo, CA
17. A. Fernández, M. Calderón, E. Barrenechea, H. Bustince, and F. Herrera Solving mult-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations Fuzzy Sets Syst. 161 23 2010 3064 3080
18. A. Fernández, M.J. del Jesus, and F. Herrera On the 2-tuples based genetic tuning performance for fuzzy rule based classification systems in imbalanced data-sets Inform. Sci. 180 8 2010 1268 1291
19. C. Ferri, J. Hernández-Orallo, and R. Modroiu An experimental comparison of performance measures for classification Pattern Recogn. Lett. 30 1 2009 27 38
20. P.A. Flach, and N. Lachiche Naive Bayesian classification of structured data Mach. Learn. 57 3 2004 233 269
21. G. Forman Quantifying counts and costs via classification Data Min. Knowl. Discov. 17 2 2008 164 206
22. J. Fürnkranz Round robin classification J. Mach. Learn. Res. 2 2002 721 747
23. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, F. Herrera, Aggregation Schemes for binarization techniques. Methods' Description, Tech. Rep., Research Group on Soft Computing and Intelligent Information Systems (2011). < http://sci2s.ugr.es/ovo-ova/AggregationMethodsDescription.pdf >.
24. 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 8 2011 1761 1776
25. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches IEEE Trans. Syst., Man, Cybernet., Part C: Appl. Rev. 42 4 2012 463 484
26. 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 12 2013 3412 3424
27. M. Galar, J. Fernandez, G. Beliakov, and H. Bustince Interval-valued fuzzy sets applied to stereo matching of color images IEEE Trans. Image Process. 20 7 2011 1949 1961
28. S. García, and F. Herrera An extension on "statistical comparisons of classifiers over multiple data sets for all pairwise comparisons J. Mach. Learn. Res. 9 2008 2677 2694
29. N. Garcia-Pedrajas, and D. Ortiz-Boyer Improving multiclass pattern recognition by the combination of two strategies IEEE Trans. Pattern Anal. Mach. Intell. 28 6 2006 1001 1006
30. D.E. Goldberg Genetic Algorithms in Search Optimization and Machine Learning 1989 Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA
31. H. He, and E.A. Garcia Learning from imbalanced data IEEE Trans. Knowl. Data Eng. 21 9 2009 1263 1284
32. P. Honeine, Z. Noumir, and C. Richard Multiclass classification machines with the complexity of a single binary classifier Signal Process. 93 5 2013 1013 1026
33. C.W. Hsu, and C.J. Lin A comparison of methods for multiclass support vector machines IEEE Trans. Neural Netw. 13 2 2002 415 425
34. G.-B. Huang, H. Zhou, X. Ding, and R. Zhang Extreme learning machine for regression and multiclass classification IEEE Trans. Syst., Man, Cybernet., Part B: Cybernet. 42 2 2012 513 529
35. E. Hüllermeier, and S. Vanderlooy Combining predictions in pairwise classification: an optimal adaptive voting strategy and its relation to weighted voting Pattern Recogn. 43 1 2010 128 142
36. N. Japkowicz, and M. Shah Evaluating Learning Algorithms: A Classification Perspective 2011 Cambridge University Press
37. S. Knerr, L. Personnaz, and G. Dreyfus Single-layer learning revisited: a stepwise procedure for building and training a neural network F. Fogelman Soulié, J. Hérault, Neurocomputing: Algorithms, Architectures and Applications vol. F68 of NATO ASI Series 1990 Springer-Verlag 41 50
38. N. Lachiche, and P.A. Flach Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves Proc. 20th International Conference on Machine Learning (ICML'03) 2003 AAAI Press
39. B. Liu, Z. Hao, and E.C.C. Tsang Nesting one-against-one algorithm based on SVMs for pattern classification IEEE Trans. Neural Netw. 19 12 2008 2044 2052
40. L. Liu, and P. Fieguth Texture classification from random features IEEE Trans. Pattern Anal. Mach. Intell. 34 2012 574 586
41. Y. Liu, Fisher consistency of multicategory support vector machines, in: 11th Int. Conf. Artificial Intelligence and Statistics (AISTAT'07), 2007.
42. A.C. Lorena, A.C. Carvalho, and J.M. Gama A review on the combination of binary classifiers in multiclass problems Artif. Intell. Rev. 30 1-4 2008 19 37
43. M. Lozano, F. Herrera, N. Krasnogor, and D. Molina Real-coded memetic algorithms with crossover hill-climbing Evol. Comput. 12 2004 273 302
44. H.H. Malik, D. Fradkin, and F. Moerchen Single pass text classification by direct feature weighting Knowl. Inform. Syst. 28 2011 79 98
45. E. Montañés, J. Barranquero, J. Díez, and J.J. del Coz Enhancing directed binary trees for multi-class classification Inform. Sci. 223 2013 42 55
46. J.G. Moreno-Torres, T. Raeder, R. Alaiz-Rodríguez, N.V. Chawla, and F. Herrera A unifying view on dataset shift in classification Pattern Recogn. 45 1 2012 521 530
47. T.K. Paul, and H. Iba Prediction of cancer class with majority voting genetic programming classifier using gene expression data IEEE/ACM Trans. Comput. Biol. Bioinformatics 6 2 2009 353 367
48. J.C. Platt Fast Training of Support Vector Machines using Sequential Minimal Optimization 1999 MIT Press Cambridge, MA, USA
49. J.C. Platt Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods Advances in Large Margin Classifiers 1999 MIT Press
50. F. Provost, and P. Domingos Tree induction for probability-based ranking Mach. Learn. 52 3 2003 199 215
51. F. Provost, and T. Fawcett Robust classification for imprecise environments Mach. Learn. 42 2001 203 231
52. O. Pujol, P. Radeva, and J. Vitria Discriminant ECOC: a heuristic method for application dependent design of error correcting output codes IEEE Trans. Pattern Anal. Mach. Intell. 28 6 2006 1007 1012
53. J.R. Quinlan C4.5: Programs for Machine Learning first ed. 1993 Morgan Kaufmann Publishers San Mateo-California
54. J. Quionero-Candela, M. Sugiyama, A. Schwaighofer, and N.D. Lawrence Dataset Shift in Machine Learning 2009 The MIT Press
55. R. Rifkin, and A. Klautau In defense of one-vs-all classification J. Mach. Learn. Res. 5 2004 101 141
56. J.A. Sáez, M. Galar, J. Luengo, and F. Herrera Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition Knowledge and Information Systems 38 1 2014 179 206
57. J. Sanz, A. Fernández, H. Bustince, and F. Herrera A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: degree of ignorance and lateral position Int. J. Approx. Reason. 52 6 2011 751 766
58. M. Shah, M. Marchand, and J. Corbeil Feature selection with conjunctions of decision stumps and learning from microarray data IEEE Trans. Pattern Anal. Mach. Intell. 34 1 2012 174 186
59. A. Tewari, and P.L. Bartlett On the consistency of multiclass classification methods J. Mach. Learn. Res. 8 2007 1007 1025
60. V. Vapnik Statistical Learning Theory 1998 Wiley New York
61. F. Wilcoxon Individual comparisons by ranking methods Biometrics Bull. 1 6 1945 80 83
62. T.F. Wu, C.J. Lin, and R.C. Weng Probability estimates for multi-class classification by pairwise coupling J. Mach. Learn. Res. 5 2004 975 1005
63. X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G.J. McLachlan, A. Ng, B. Liu, P.S. Yu, Z.-H. Zhou, M. Steinbach, D.J. Hand, and D. Steinberg Top 10 algorithms in data mining Knowl. Inform. Syst. 14 2007 1 37
64. J. Yang, I.W. Tsang, Hierarchical maximum margin learning for multi-class classification, in: Proc. 27th Conf. Uncertainty in Artificial Intelligence (UAI 2011), 2011.
65. Z.-H. Zhou, and X.-Y. Liu Training cost-sensitive neural networks with methods addressing the class imbalance problem IEEE Trans. Knowl. Data Eng. 18 1 2006 63 77