A genetic learning of the fuzzy rule-based classification system granularity for highly imbalanced data-sets

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2009, Pages 1689-1694

  Villar, Pedro ^a   Fernández, Alberto ^a   Herrera, Francisco ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS DISTRIBUTIONS; DATA SETS; FINE GRANULARITY; FIXED NUMBERS; FUZZY RULE BASED CLASSIFICATION SYSTEMS; GENETIC LEARNING; GRANULARITY LEVELS; IMBALANCED DATA; REAL APPLICATIONS;

DECISION TREES; FUZZY RULES; FUZZY SYSTEMS;

LABELS;

DECISION MAKING; DISPERSIONS; LABELS; PROBLEM SOLVING; TREES;

EID: 71249086896     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2009.5277304     Document Type: Conference Paper

References (28)

1. Y.H. Liu and Y.T. Chen. Face recognition using total margin-based adaptive fuzzy support vector machines. IEEE Transactions on Neural Networks, 18(1):178-192, 2007.
2. Y. M. Huang, C. M. Hung, and H. C. Jiau. Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem. Nonlinear Analysis: Real World Applications, 7(4):720-747, 2006.
3. M.A. Mazurowski, P.A. Habas, J.M. Zurada, J.Y. Lo, J.A. Baker, and G.D. Tourassi. Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Networks, 21(2-3):427-436, 2008.
4. H. Ishibuchi, T. Nakashima, and M. Nii. Classification and modeling with linguistic information granules: Advanced approaches to linguistic Data Mining. Springer-Verlag, 2004.
5. A. Fernández, S. García, M.J. del Jesus, and F. Herrera. A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets and Systems, 159(18):2378-2398, 2008.
6. O. Cordón, F. Herrera and P. Villar. Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing International Journal of Approximate Reasoning, vol. 25(3):187-215, 2000.
7. O. Cordón, F. Herrera, P. Villar. Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base IEEE Transactions on Fuzzy Systems 9(4):667-674, 2001
8. O. Cordón, F. Herrera, L. Magdalena, P. Villar. A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule-based system data base Information Sciences 136:85-107, 2001.
9. E. Zhou, A. Khotanzad. Fuzzy classifier design using genetic algorithms Pattern Recognition 40(12): 3401-3414, 2007.
10. I. Walter, F. Gomide. Genetic fuzzy systems to evolve interaction strategies in multiagent systems International Journal of Intelligent Systems 22(9): 971-991, 2007
11. Z. Chi, H. Yan, and T. Pham. Fuzzy algorithms with applications to image processing and pattern recognition. World Scientific, 1996.
12. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo-California, 1993.
13. G.E.A.P.A. Batista, R.C. Prati and M.C. Monard. A Study of the Behaviour of Several Methods for Balancing Machine Learning Training Data. SIGKDD Explorations, 6(1):20-29, 2004.
14. A. Estabrooks, T. Jo, and N. Japkowicz. A multiple resampling method for learning from imbalanced data sets. Computational Intelligence, 20(1):18-36, 2004.
15. C.T. Su and Y.H. Hsiao. An evaluation of the robustness of MTS for imbalanced data. IEEE Transactions on Knowledge Data Engineering, 19(10):1321-1332, 2007.
16. A. Asuncion and D.J. Newman. UCI machine learning repository, 2007. University of California, Irvine, School of Information and Computer Sciences. URL http://www.ics.uci.edu/~mlearn/MLRepository.html
17. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligent Research, 16:321-357, 2002.
18. J. Demšar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30, 2006.
19. S. García and F. Herrera An Extension on "Statistical Comparisons of Classifiers over Multiple data sets" for all Pairwise Comparisons. Journal of Machine Learning Research, 9:2607-2624, 2008.
20. 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 Computing, in press. Doi: 10.1007/s00500-008-0392-y, 2009.
21. N. V. Chawla, N. Japkowicz, and A. Kolcz. Editorial: special issue on learning from imbalanced data sets. SIGKDD Explorations, 6(1):1-6, 2004.
22. G. M. Weiss. Mining with rarity: a unifying framework. SIGKDD Explorations, 6(1):7-19, 2004.
23. G.M. Weiss and F. Provost. Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research, 19:315-354, 2003.
24. V. García, R.A. Mollineda, and J. S. Sánchez. On the k-NN performance in a challenging scenario of imbalance and overlapping. Pattern Analysis Applications, 11(3-4):269-280, 2008.
25. A. Orriols-Puig and E. Bernadó-Mansilla. Evolutionary rule-based systems for imbalanced datasets. Soft Computing, 13(3):213-225, 2009.
26. J. Huang and C. X. Ling. Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3):299-310, 2005.
27. H. Ishibuchi and T. Yamamoto. Rule Weight Specification in Fuzzy Rule-Based Classification Systems. IEEE Transactions on Fuzzy Systems, 13:428-435, 2005.
28. D. Sheskin. Handbook of parametric and nonparametric statistical procedures. Chapman & Hall/CRC, second edition, 2006.