Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme

Knowledge-Based Systems

Volumn 125, Issue , 2017, Pages 53-63

  Zhang, Zhong Liang ^a,b   Luo, Xing Gang ^a,b   García, Salvador ^c   Tang, Jia Fu ^b   Herrera, Francisco ^c,d  

^a HANGZHOU DIANZI UNIVERSITY   (China)

^b NORTHEASTERN UNIVERSITY   (China)

^c UNIVERSITY OF GRANADA   (Spain)

^d KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Decomposition strategies; Dynamic ensemble selection; Multi classification; One versus One; Pairwise learning

Indexed keywords

CLASSIFIERS; LEARNING SYSTEMS; PROBLEM SOLVING;

DECOMPOSITION STRATEGY; DYNAMIC ENSEMBLE SELECTIONS; MULTI-CLASSIFICATION; ONE-VERSUS-ONE; PAIRWISE LEARNING;

CLASSIFICATION (OF INFORMATION);

EID: 85017093028     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2017.03.026     Document Type: Article

References (59)

1. [1] Alcalá-Fdez, J., Fernandez, A., Luengo, J., Derrac, J., García, S., Sanchez, L., Herrera, F., KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult-Valued Log S 17:2-3 (2011), 255–287.
2. [2] Alcalá-Fdez, J., Sánchez, L., García, S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fernández, J.C., Herrera, F., KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. 13:3 (2009), 307–318.
3. [3] Allwein, E.L., Schapire, R.E., Singer, Y., Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1 (2001), 113–141.
4. [4] Breiman, L., Forests, Random, Mach. Learn. 45:1 (2001), 5–32.
5. [5] Britto, A.S., Sabourin, R., Oliveira, L.E.S., Dynamic selection of classifiers-A comprehensive review. Pattern Recognit. 47:11 (2014), 3665–3680.
6. [6] Chang, C.C., Lin, C.J., LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2:3 (2011), 1–27.
7. [7] Clark, P., Boswell, R., Rule induction with CN2: some recent improvements. Kodratoff, Y., (eds.) Machine Learning — EWSL-91, 1991, Springer, Berlin Heidelberg, 151–163.
8. [8] Demsar, J., Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (2006), 1–30.
9. [9] Derrac, J., García, S., Molina, D., Herrera, F., A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1:1 (2011), 3–18.
10. [10] Didaci, L., Giacinto, G., Roli, F., Marcialis, G.L., A study on the performances of dynamic classifier selection based on local accuracy estimation. Pattern Recognit. 38:11 (2005), 2188–2191.
11. [11] Dietterich, T., Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2 (1995), 263–286.
12. [12] dos Santos, E.M., Sabourin, R., Maupin, P., Ambiguity-guided dynamic selection of ensemble of classifiers. 10th International Conference on Information Fusion, 2007, 1–8.
13. [13] Dos Santos, E.M., Sabourin, R., Maupin, P., A dynamic overproduce-and-choose strategy for the selection of classifier ensembles. Pattern Recognit. 41:10 (2008), 2993–3009.
14. [14] Engchuan, W., Chan, J.H., Pathway activity transformation for multi-class classification of lung cancer datasets. Neurocomputing 165 (2015), 81–89.
15. [15] Fernández, A., Calderón, M., Barrenechea, E., Bustince, H., Herrera, F., Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations. Fuzzy Sets Syst. 161:23 (2010), 3064–3080.
16. [16] Fernández, A., López, V., Galar, M., del Jesus, M.J., Herrera, F., Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches. Knowl.-Based Syst. 42 (2013), 97–110.
17. [17] Friedman, J., Another Approach to Polychotomous Classification., 1996, Department of Statistics, Stanford University Technical report.
18. [18] Galar, M., Fernández, A., Barrenechea, E., Herrera, F., DRCW-OVO: distance-based relative competence weighting combination for One-vs-One strategy in multi-class problems. Pattern Recognit. 48:1 (2015), 28–42.
19. [19] Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F., An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit. 44:8 (2011), 1761–1776.
20. [20] Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F., Dynamic classifier selection for One-vs-One strategy: avoiding non-competent classifiers. Pattern Recognit. 46:12 (2013), 3412–3424.
21. [21] Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F., NMC: nearest matrix classification – a new combination model for pruning One-vs-One ensembles by transforming the aggregation problem. Inf. Fusion 36 (2017), 26–51.
22. [22] García, S., Fernández, A., Julián, L., Herrera, F., Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180:10 (2010), 2044–2064.
23. [23] Giacinto, G., Roli, F., Methods for dynamic classifier selection. International Conference on Image Analysis and Processing, 1999, 659–664.
24. [24] Gomez, J.C., Moens, M.F., Minimizer of the reconstruction error for multi-class document categorization. Expert Syst. Appl. 41:3 (2014), 861–868.
25. [25] Hüllermeier, E., Brinker, K., Learning valued preference structures for solving classification problems. Fuzzy Sets Syst. 159:18 (2008), 2337–2352.
26. [26] Hüllermeier, E., Vanderlooy, S., Combining predictions in pairwise classification: an optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognit. 43:1 (2010), 128–142.
27. [27] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11:1 (2009), 10–18.
28. [28] Hansen, L., Liisberg, C., Salamon, P., The error-reject tradeoff. Open Syst. Inf. Dyn. 4:2 (1997), 159–184.
29. [29] Huhn, J.C., Hullermeier, E., FR3: a fuzzy rule learner for inducing reliable classifiers. IEEE T Fuzzy Syst. 17:1 (2009), 138–149.
30. [30] Ji, Y., Sun, S., Multitask multiclass support vector machines: model and experiments. Pattern Recognit. 46:3 (2013), 914–924.
31. [31] Kang, S., Cho, S., Kang, P., Constructing a multi-class classifier using one-against-one approach with different binary classifiers. Neurocomputing 149:Part B (2015), 677–682.
32. [32] Knerr, S., Personnaz, L., Dreyfus, G., Single-layer learning revisited: a stepwise procedure for building and training a neural network. Soulié, F., Hérault, J., (eds.) Neurocomputing, 1990, Springer, Berlin Heidelberg, 41–50.
33. [33] Ko, A.H.R., Sabourin, R., Britto, J.A.S., From dynamic classifier selection to dynamic ensemble selection. Pattern Recognit. 41:5 (2008), 1718–1731.
34. [34] Kuncheva, L.I., Whitaker, C.J., Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51:2 (2003), 181–207.
35. [35] Lorena, A., de Carvalho, A.P.L.F., Gama, J.P., A review on the combination of binary classifiers in multiclass problems. Artif. Intell. Rev. 30:1-4 (2008), 19–37.
36. [36] Lysiak, R., Kurzynski, M., Woloszynski, T., Probabilistic approach to the dynamic ensemble selection using measures of competence and diversity of base classifiers. Proceedings of the 6th International Conference on Hybrid Artificial Intelligent Systems - Volume Part II, 2011, Springer-Verlag, Wroclaw, Poland, 229–236.
37. [37] Lysiak, R., Kurzynski, M., Woloszynski, T., Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers. Neurocomputing 126 (2014), 29–35.
38. [38] Mendialdua, I., Martínez Otzeta, J.M., Rodriguez Rodriguez, I., Ruiz Vazquez, T., Sierra, B., Dynamic selection of the best base classifier in One versus One. Knowl.-Based Syst. 85 (2015), 298–306.
39. [39] Moreno Torres, J.G., Saez, J.A., Herrera, F., Study on the impact of partition-induced dataset shift on-fold cross-validation. IEEE Trans. Neural Networks Learn. Syst. 23:8 (2012), 1304–1312.
40. [40] Mousavi, R., Eftekhari, M., A new ensemble learning methodology based on hybridization of classifier ensemble selection approaches. Appl. Soft Comput. 37 (2015), 652–666.
41. [41] Orlovsky, S.A., Decision-making with a fuzzy preference relation. Fuzzy Sets Syst. 1:3 (1978), 155–167.
42. [42] Pasti, R., de Castro, L.N., Bio-inspired and gradient-based algorithms to train MLPs: the influence of diversity. Inf. Sci. 179:10 (2009), 1441–1453.
43. [43] Rokach, L., Decision forest: twenty years of research. Inf. Fusion 27 (2016), 111–125.
44. [44] Santana, A., Soares, R.G.F., Canuto, A.M.P., Souto, M.C.P.d., A dynamic classifier selection method to build ensembles using accuracy and diversity. Ninth Brazilian Symposium on Neural Networks, 2006, 36–41.
45. [45] Smits, P.C., Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection. IEEE Trans. Geosci. Remote Sens. 40:4 (2002), 801–813.
46. [46] Sun, S., Local within-class accuracies for weighting individual outputs in multiple classifier systems. Pattern Recognit. Lett. 31:2 (2010), 119–124.
47. [47] Ting Fan, W., Chih Jen, L., Weng, R.C., Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5 (2004), 975–1005.
48. [48] Vladimir Naumovich, V., Statistical Learning Theory. 1998, Wiley, New York.
49. [49] West, D., Dellana, S., Diversity of ability and cognitive style for group decision processes. Inf. Sci. 179:5 (2009), 542–558.
50. [50] Wilcoxon, F., Individual comparisons by ranking methods. Biom. Bull. 1:6 (1945), 80–83.
51. [51] Wilson, D.R., Martinez, T.R., Improved heterogeneous distance functions. J. Artif. Intell. Res. 6 (1997), 1–34.
52. [52] Windeatt, T., Diversity measures for multiple classifier system analysis and design. Inf. Fusion 6:1 (2005), 21–36.
53. [53] Woloszynski, T., Kurzynski, M., On a new measure of classifier competence applied to the design of multiclassifier systems. Proceedings of the 15th International Conference on Image Analysis and Processing, 2009, Springer-Verlag, Vietri sul Mare, Italy, 995–1004.
54. [54] Woloszynski, T., Kurzynski, M., A probabilistic model of classifier competence for dynamic ensemble selection. Pattern Recognit. 44:10–11 (2011), 2656–2668.
55. [55] Woloszynski, T., Kurzynski, M., Podsiadlo, P., Stachowiak, G.W., A measure of competence based on random classification for dynamic ensemble selection. Inf. Fusion 13:3 (2012), 207–213.
56. [56] Woods, K., Philip Kegelmeyer, J.W., Bowyer, K., Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal. Mach. Intell. 19:4 (1997), 405–410.
57. [57] Woźniak, M., Graña, M., Corchado, E., A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16 (2014), 3–17.
58. [58] Zhou, L., Wang, Q., Fujita, H., One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies. Inf. Fusion 36 (2017), 80–89.
59. [59] Zini, L., Noceti, N., Fusco, G., Odone, F., Structured multi-class feature selection with an application to face recognition. Pattern Recognit. Lett. 55 (2015), 35–41.