Enhancing Multiclass Classification in FARC-HD Fuzzy Classifier: On the Synergy Between n-Dimensional Overlap Functions and Decomposition Strategies

IEEE Transactions on Fuzzy Systems

Volumn 23, Issue 5, 2015, Pages 1562-1580

  Elkano, Mikel ^a   Galar, Mikel ^a   Sanz, Jose Antonio ^a   Fernández, Alberto ^b   Barrenechea, Edurne ^a   Herrera, Francisco ^c,d   Bustince, Humberto ^a  

^a PUBLIC UNIVERSITY OF NAVARRE   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

^c UNIVERSITY OF GRANADA   (Spain)

^d KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Aggregations; fuzzy rule based classification systems; multiclassification; one versus one; overlaps

Indexed keywords

AGGLOMERATION; BIOINFORMATICS; COMPUTER AIDED DIAGNOSIS; COMPUTER VISION; FUZZY INFERENCE; FUZZY LOGIC; FUZZY RULES; FUZZY SETS; FUZZY SYSTEMS; PROBLEM SOLVING;

FUZZY RULE BASED CLASSIFICATION SYSTEMS; HIGH-DIMENSIONAL PROBLEMS; MULTI-CLASS CLASSIFICATION; MULTI-CLASSIFICATION; MULTICLASS CLASSIFICATION PROBLEMS; ONE-VERSUS-ONE; OVERLAPS; PERFORMANCE OF CLASSIFIER;

CLASSIFICATION (OF INFORMATION);

EID: 84975256721     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2014.2370677     Document Type: Article

References (69)

1. H. Ishibuchi, T. Nakashima, and M. Nii, Classification and Modeling With Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining. New York, NY, USA: Springer-Verlag, 2004.
2. F. Chávez, F. Fernández, R. Alcalá, J. Alcalá-Fdez, G. Olague, and F. Herrera, " Hybrid laser pointer detection algorithm based on template matching and fuzzy rule-based systems for domotic control in real home enviroments," Appl. Intell., vol. 36, no. 2, pp. 407-423, 2012.
3. C. Tsang, S. Kwong, and H. Wang, "Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection," Pattern Recog., vol. 40, no. 9, pp. 2373-2391, 2007.
4. T. Nakashima, G. Schaefer, and Y. Yokota, "A weighted fuzzy classifier and its application to image processing tasks," Fuzzy Sets Syst., vol. 158, no. 3, pp. 284-294, 2007.
5. J. Sanz, M. Galar, A. Jurio, A. Brugos, M. Pagola, and H. Bustince, "Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system," Appl. Soft Comput. J., vol. 20, pp. 103-111, 2014.
6. M. Galar, J. Sanz, M. Pagola, H. Bustince, and F. Herrera, "A preliminary study on fingerprint classification using fuzzy rule-based classification systems," in Proc. IEEE Int. Conf. Fuzzy Syst., 2014, pp. 554-560.
7. X.-X. Niu and C. Y. Suen, "A novel hybrid CNN-SVM classifier for recognizing handwritten digits," Pattern Recog., vol. 45, no. 4, pp. 1318-1325, 2012.
8. V. Bolón-Canedo, N. S.-M. No, and A. Alonso-Betanzos, "An ensemble of filters and classifiers formicroarray data classification," Pattern Recog., vol. 45, no. 1, pp. 531-539, 2012.
9. 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-versus-one and one-versus-all schemes," Pattern Recog., vol. 44, no. 8, pp. 1761-1776, 2011.
10. A. Lorena, A. Carvalho, and J. Gama, "A review on the combination of binary classifiers in multiclass problems," Artif. Intell. Rev., vol. 30, no. 1-4, pp. 19-37, 2008.
11. E. L. Allwein, R. E. Schapire, and Y. Singer, "Reducing multiclass to binary: A unifying approach for margin classifiers," J. Mach. Learning Res., vol. 1, pp. 113-141, 2000.
12. J. Alcalá-Fdez, R. Alcalá, and F. Herrera, "A fuzzy association rulebased classification model for high-dimensional problems with genetic rule selection and lateral tuning," IEEE Trans. Fuzzy Syst., vol. 19, no. 5, pp. 857-872, Oct. 2011.
13. J. Fürnkranz, "Round robin classification," J. Mach. Learning Res., vol. 2, pp. 721-747, 2002.
14. J. Fürnkranz, "Round robin ensembles," Intell. Data Anal., vol. 7, no. 5, pp. 385-403, 2003.
15. 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-versus-one decomposition," Knowledge Inform. Syst., vol. 38, no. 1, pp. 179-206, 2014.
16. M. Galar, A. Fernández, E. Barrenechea, and F. Herrera, "Empowering difficult classes with a similarity-based aggregation in multi-class classification problems," Inform. Sci., vol. 264, pp. 135-157, 2014.
17. O. Cordón, M. J. del Jesus, and F. Herrera, "A proposal on reasoning methods in fuzzy rule-based classification systems," Int. J. Approximate Reasoning, vol. 20, no. 1, pp. 21-45, 1999.
18. E. Hüllermeier and S. Vanderlooy, "Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting," Pattern Recog., vol. 43, no. 1, pp. 128-142, 2010.
19. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, "Dynamic classifier selection for one-versus-one strategy: Avoiding noncompetent classifiers," Pattern Recog., vol. 46, no. 12, pp. 3412-3424, 2013.
20. H. Bustince, J. Fernandez, R. Mesiar, J. Montero, and R. Orduna, "Overlap functions," Nonlinear Anal.: Theory, Methods Appl., vol. 72, no. 3-4, pp. 1488-1499, 2010.
21. J. Alcalá-Fdez, A. Fernandez, 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., vol. 17, vol. 2/3, pp. 255-287, 2011.
22. S. García, A. Fernández, J. Luengo, and F. Herrera, "A study of statistical techniques and performance measures for genetics-based machine learning: Accuracy and interpretability," Soft Comput., vol. 13, no. 10, pp. 959-977, 2009.
23. 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., vol. 180, no. 10, pp. 2044-2064, 2010.
24. J. Hühn and E. Hüllermeier, "FURIA: An algorithm for unordered fuzzy rule induction," Data Mining Knowledge Discovery, vol. 19, no. 3, pp. 293-319, 2009.
25. J. Sanz, A. Fernández, H. Bustince, and F. Herrera, "IVTURS: A linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection," IEEE Trans. Fuzzy Syst., vol. 21, no. 3, pp. 399-411, Jun. 2013.
26. R. Senge and E. Hüllermeier, "Top-down induction of fuzzy pattern trees," IEEE Trans. Fuzzy Syst., vol. 19, no. 2, pp. 241-252, Apr. 2011.
27. H. Ishibuchi and T. Yamamoto, "Rule weight specification in fuzzy rulebased classification systems," IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 428-435, Aug. 2005.
28. R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. Int. Conf. Very Large Data Bases, Santiago de Chile, Chile, Sep. 1994, pp. 487-499.
29. B. Kavsek and N. Lavrac, "Apriori-sd: Adapting association rule learning to subgroup discovery," Appl. Artif. Intell., vol. 20, no. 7, pp. 543-583, 2006.
30. R. Alcalá, J. Alcalá-Fdez, and F. Herrera, "A proposal for the genetic lateral tuning of linguistic fuzzy systems and its interaction with rule selection," IEEE Trans. Fuzzy Syst., vol. 15, no. 4, pp. 616-635, Aug. 2007.
31. J. Friedman, (1996). "Another approach to polychotomous classification," Dept. Statist., Stanford Univ., Tech. Rep., 1996. [Online]. Available: http://www-stat.stanford.edu/jhf/ftp/poly.ps.Z
32. 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., vol. 161, no. 23, pp. 3064-3080, 2010.
33. E. Hüllermeier and K. Brinker, "Learning valued preference structures for solving classification problems," Fuzzy Sets Syst., vol. 159, no. 18, pp. 2337-2352, 2008.
34. J. C. Huhn and E. Hullermeier, "FR3: A fuzzy rule learner for inducing reliable classifiers," IEEE Trans. Fuzzy Syst., vol. 17, no. 1, pp. 138-149, Feb. 2009.
35. L. I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. New York, NY, USA: Wiley-Interscience, 2004.
36. M. Woźniak, M. Graña, and E. Corchado, "A survey of multiple classifier systems as hybrid systems," Inform. Fusion, vol. 16, pp. 3-17, 2014.
37. L. I. Kuncheva, "Diversity in multiple classifier systems," Inform. Fusion, vol. 6, no. 1, pp. 3-4, 2005.
38. L. Breiman, "Bagging predictors," Mach. Learning, vol. 24, pp. 123-140, 1996.
39. R. E. Schapire, "The strength of weak learnability," Mach. Learning, vol. 5, pp. 197-227, 1990.
40. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," J. Comput. Syst. Sci., vol. 55, no. 1, pp. 119-139, 1997.
41. H. Ishibuchi and Y. Nojima, "Fuzzy ensemble design through multiobjective fuzzy rule selection," in Multi-Objective Machine Learning, (Studies in Computational Intelligence), Y. Jin, Ed., Berlin, Germany : Springer, 2006, vol. 16, pp. 507-530.
42. P. Bonissone, J. M. Cadenas, M. C. Garrido, and R. A. Díaz-Valladares, "A fuzzy random forest," Int. J. Approximate Reasoning, vol. 51, no. 7, pp. 729-747, 2010.
43. K. Trawiński, O. Cordón, and A. Quirin, "On designing fuzzy rule-based multiclassification systems by combining FURIA with bagging and feature selection," Int. J. Uncertainty, Fuzziness Knowledge-Based Syst., vol. 19, no. 4, pp. 589-633, 2011.
44. K. Trawinski, O. Cordon, L. Sanchez, and A. Quirin, "A genetic fuzzy linguistic combination method for fuzzy rule-based multiclassifiers," IEEE Trans. Fuzzy Syst., vol. 21, no. 5, pp. 950-965, Oct. 2013.
45. J. M. Cadenas, M. C. Garrido, R. Martínez, and P. P. Bonissone, "Extending information processing in a fuzzy random forest ensemble," Soft Comput., vol. 16, no. 5, pp. 845-861, 2012.
46. A. M. Palacios, L. Sánchez, and I. Couso, "Boosting of fuzzy rules with low quality data," J. Multiple-Valued Logic Soft Comput., vol. 19, no. 5-6, pp. 591-619, 2012.
47. K. Trawiński, O. Cordón, and A. Quirin, "A study on the use of multiobjective genetic algorithms for classifier selection in FURIA-based fuzzy multiclassifiers," Int. J. Comput. Intell. Syst., vol. 5, no. 2, pp. 231-253, 2012.
48. B. Soua, A. Borgi, and M. Tagina, "An ensemble method for fuzzy rulebased classification systems," Knowledge Inform. Syst., vol. 36, no. 2, pp. 385-410, 2013.
49. E. Hüllermeier and K. Brinker, "Learning valued preference structures for solving classification problems," Fuzzy Sets Syst., vol. 159, no. 18, pp. 2337-2352, 2008.
50. H. Ishibuchi, T. Yamamoto, and T. Nakashima, "Hybridization of fuzzy GBML approaches for pattern classification problems," IEEE Trans. Syst., Man Cybern. B, vol. 35, no. 2, pp. 359-365, Apr. 2005.
51. A. González and R. Perez, "SLAVE: A genetic learning system based on an iterative approach," IEEE Trans. Fuzzy Syst., vol. 7, no. 2, pp. 176-191, Apr. 1999.
52. Y.-C. Chen, N. R. Pal, and I.-F. Chung, "An integrated mechanism for feature selection and fuzzy rule extraction for classification," IEEE Trans. Fuzzy Syst., vol. 20, no. 4, pp. 683-698, Aug. 2012.
53. J. Castro-Schez, M. J. M., R. Miguel, and X. Luo, "Knowledge acquisition based on learning of maximal structure fuzzy rules," Knowledge-Based Syst., vol. 44, pp. 112-120, 2013.
54. A. Acilar and A. Arslan, "A novel approach for designing adaptive fuzzy classifiers based on the combination of an artificial immune network and a memetic algorithm," Inform. Sci., vol. 264, pp. 158-181, 2014.
55. M. J. Gacto, R. Alcalá, and F. Herrera, "Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures," Inform. Sci., vol. 181, no. 20, pp. 4340-4360, 2011.
56. Z. H. Deng, L. B. Cao, Y. Z. Jiang, and S. T. Wang, "Minimax probability TSK fuzzy system classifier: A more transparent and highly interpretable classification model," IEEE Trans. Fuzzy Syst., to be published.
57. D. Paternain, M. Pagola, J. Fernandez, R. Mesiar, G. Beliakov, and H. Bustince, "Brain MRI thresholding using incomparability and overlap functions," in Proc. 11th Int. Conf. Intell. Syst. Design Appl., 2011, pp. 808-812.
58. A. Jurio, H. Bustince, M. Pagola, A. Pradera, and R. R. Yager, "Some properties of overlap and grouping functions and their application to image thresholding," Fuzzy Sets Syst., vol. 229, pp. 69-90, 2013.
59. D. Paternain, J. Fernandez, H. Bustince, R. Mesiar, and G. Beliakov, "Construction of image reduction operators using averaging aggregation functions," Fuzzy Sets Syst., to be published.
60. H. Bustince, M. Pagola, R. Mesiar, E. Hullermeier, and F. Herrera, "Grouping, overlap, and generalized bientropic functions for fuzzy modeling of pairwise comparisons," IEEE Trans. Fuzzy Syst., vol. 20, no. 3, pp. 405-415, Jun. 2012.
61. G. Beliakov, A. Pradera, and T. Calvo, Aggregation Functions: A Guide for Practitioners (Studies in Fuzziness and Soft Computing). Berlin, Germany: Springer, 2007, vol. 221.
62. J. Moreno-Torres, T. Raeder, R. Alaiz-Rodríguez, N. Chawla, and F. Herrera, "A unifying view on dataset shift in classification," Pattern Recog., vol. 45, no. 1, pp. 521-530, 2012.
63. V. López, A. Fernández, and F. Herrera, "On the importance of the validation technique for classification with imbalanced datasets: Addressing covariate shift when data is skewed," Inform. Sci., vol. 257, pp. 1-13, 2014.
64. J. Moreno-Torres, J. Saez, and F. Herrera, "Study on the impact of partition-induced dataset shift on k-fold cross-validation," IEEE Trans. Neural Netw. Learning Syst., vol. 23, no. 8, pp. 1304-1312, Aug. 2012.
65. W. W. Cohen, "Fast effective rule induction," presented at the 12th Int. Conf. Mach. Learn., Lake Tahoe, CA, USA, 1995.
66. J. Cohen, "A coefficient of agreement for nominal scales," Educational Psychological Meas., vol. 20, no. 1, pp. 37-46, 1960.
67. F. Wilcoxon, "Individual comparisons by ranking methods," Biometrics, vol. 1, no. 6, pp. 80-83, 1945.
68. J. L. Hodges and E. L. Lehmann, "Ranks methods for combination of independent experiments in analysis of variance," Annu. Math. Statist., vol. 33, pp. 482-497, 1962.
69. S. Holm, "A simple sequentially rejective multiple test procedure," Scandinavian J. Statist., vol. 6, pp. 65-70, 1979.