A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets

Knowledge-Based Systems

Volumn 38, Issue , 2013, Pages 85-104

  López, Victoria ^a   Fernández, Alberto ^b   Del Jesus, María José ^b   Herrera, Francisco ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

Author keywords

Borderline examples; Fuzzy rule based classification systems; Genetic rule selection; Hierarchical fuzzy partitions; Imbalanced data sets; Tuning

Indexed keywords

BORDERLINE EXAMPLES; FUZZY RULE BASED CLASSIFICATION SYSTEMS; HIERARCHICAL FUZZY; IMBALANCED DATA-SETS; RULE SELECTION;

ALGORITHMS; DECISION TREES; FUZZY RULES; GENETIC PROGRAMMING; HIERARCHICAL SYSTEMS; LINGUISTICS; TUNING;

CLASSIFICATION (OF INFORMATION);

EID: 84871621085     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2012.08.025     Document Type: Article

References (57)

1. 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 Transactions on Fuzzy Systems 15 2007 616 635
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 Journal of Multi-Valued Logic and Soft Computing 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.C. Fernández, and F. Herrera KEEL: a software tool to assess evolutionary algorithms for data mining problems Soft Computing 13 2009 307 318
4. R. Barandela, J.S. Sánchez, V. García, and E. Rangel Strategies for learning in class imbalance problems Pattern Recognition 36 2003 849 851
5. A. Bastian How to handle the flexibility of linguistic variables with applications International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2 1994 463 484
6. 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 2004 20 29
7. F.J. Berlanga, A.J. Rivera, M.J. del Jesus, and F. Herrera GP-COACH: genetic programming-based learning of compact and accurate fuzzy rule-based classification systems for high-dimensional problems Information Sciences 180 2010 1183 1200
8. J. Charles Automatic recognition of complete palynomorphs in digital images Machine Vision and Applications 22 2011 53 60
9. 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 2002 321 357
10. Z. Chi, H. Yan, and T. Pham Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition 1996 World Scientific
11. O. Cordón, and F. Herrera A proposal for improving the accuracy of linguistic modeling IEEE Transactions on Fuzzy Systems 8 2000 335 344
12. O. Cordón, F. Herrera, F. Hoffmann, and L. Magdalena Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases Advances in Fuzzy Systems-Applications and Theory vol. 19 2001 World Scientific
13. O. Cordón, F. Herrera, and 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 2001 667 674
14. O. Cordon, F. Herrera, and I. Zwir Linguistic modeling by hierarchical systems of linguistic rules IEEE Transactions on Fuzzy Systems 10 2002 2 20
15. O. Cordón, M. del Jesus, and F. Herrera A proposal on reasoning methods in fuzzy rule-based classification systems International Journal of Approximate 20 1999 21 45
16. C. Drummond, R.C. Holte, C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling, in: Proceedings of the International Conference on Machine Learning (ICML 2003) Workshop on Learning from Imbalanced Data Sets II, 2003, pp. 1-8.
17. C. Elkan, The foundations of cost-sensitive learning, in: Proceedings of the 17th IEEE International Joint Conference on Artificial Intelligence (IJCAI'01), 2001, pp. 973-978.
18. L.J. Eshelman The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination Foudations of Genetic Algorithms 1990 Morgan Kaufmann 265 283
19. A. Fernández, S. García, F. Herrera, Addressing the classification with imbalanced data: open problems and new challenges on class distribution, in: Proceedings of the 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS'11), 2011, pp. 1-10.
20. 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 2008 2378 2398
21. A. Fernández, M.J. del Jesus, and F. Herrera Hierarchical fuzzy rule based classification systems with genetic rule selection for imbalanced data-sets International Journal of Approximate Reasoning 50 2009 561 577
22. 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 Information Sciences 180 2010 1268 1291
23. S. García, J. Derrac, I. Triguero, C.J. Carmona, and F. Herrera Evolutionary-based selection of generalized instances for imbalanced classification Knowledge-Based Systems 25 2012 3 12
24. 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 13 2009 959 977
25. 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 2008 2607 2624
26. V. García, R. Mollineda, and J.S. Sánchez On the k-NN performance in a challenging scenario of imbalance and overlapping Pattern Analysis Applications 11 2008 269 280
27. V. García, J. Sánchez, and R. Mollineda On the effectiveness of preprocessing methods when dealing with different levels of class imbalance Knowledge-Based Systems 25 2012 13 21
28. N. García-Pedrajas, J. Pérez-Rodríguez, M. García-Pedrajas, D. Ortiz-Boyer, and C. Fyfe Class imbalance methods for translation initiation site recognition in DNA sequences Knowledge-Based Systems 25 2012 22 34
29. H. He, and E.A. Garcia Learning from imbalanced data IEEE Transactions on Knowledge and Data Engineering 21 2009 1263 1284
30. F. Herrera Genetic fuzzy systems: taxonomy, current research trends and prospects Evolutionary Intelligence 1 2008 27 46
31. F. Herrera, M. Lozano, and A.M. Sánchez A taxonomy for the crossover operator for real-coded genetic algorithms: an experimental study International Journal of Intelligent Systems 18 2003 309 338
32. F. Herrera, and L. Martínez A 2-tuple fuzzy linguistic representation model for computing with words IEEE Transactions on Fuzzy Systems 8 2000 746 752
33. H. Ishibuchi, and T. Nakashima Effect of rule weights in fuzzy rule-based classification systems IEEE Transactions on Fuzzy Systems 9 2001 506 515
34. H. Ishibuchi, T. Nakashima, and M. Nii Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining 2004 Springer-Verlag
35. H. Ishibuchi, K. Nozaki, N. Yamamoto, and H. Tanaka Selecting fuzzy if-then rules from classification problems using genetic algorithms IEEE Transactions on Fuzzy Systems 9 1995 260 270
36. H. Ishibuchi, and T. Yamamoto Rule weight specification in fuzzy rule-based classification systems IEEE Transactions on Fuzzy Systems 13 2005 428 435
37. N. Japkowicz, and S. Stephen The class imbalance problem: a systematic study Intelligent Data Analysis Journal 6 2002 429 450
38. W. Khreich, E. Granger, A. Miri, and R. Sabourin Iterative boolean combination of classifiers in the ROC space: an application to anomaly detection with HMMs Pattern Recognition 43 2010 2732 2752
39. J.R. Koza Genetic Programming: On the Programming of Computers by Means of Natural Selection 1992 The MIT Press
40. M. Kubat, S. Matwin, Addressing the curse of imbalanced training sets: one-sided selection, in: 14th International Conference on Machine Learning(ICML97), 1997, pp. 179-186.
41. J. Liu, Q. Hu, and D. Yu A comparative study on rough set based class imbalance learning Knowledge-Based Systems 21 2008 753 763
42. V. López, A. Fernández, J.G. Moreno-Torres, and F. Herrera Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics Expert Systems with Applications 39 2012 6585 6608
43. L. Magdalena, and F. Monasterio-Huelin A fuzzy logic controller with learning through the evolution of its knowledge base International Journal of Approximate Reasoning 16 1997 335 358
44. K. Napierala, J. Stefanowski, S. Wilk, Learning from imbalanced data in presence of noisy and borderline examples, in: 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC2010), 2010, pp. 158-167.
45. S. Oh, M. Lee, and B.T. Zhang Ensemble learning with active example selection for imbalanced biomedical data classification IEEE/ACM Transactions on Computational Biology and Bioinformatics 8 2011 316 325
46. A. Orriols-Puig, and E. Bernadó-Mansilla Evolutionary rule-based systems for imbalanced datasets Soft Computing 13 2009 213 225
47. J. Quinlan C4.5: Programs for Machine Learning. 1993 Morgan Kauffman
48. J. Ren ANN vs. SVM: which one performs better in classification of MCCs in mammogram imaging Knowledge-Based Systems 26 2012 144 153
49. C. Seiffert, T.M. Khoshgoftaar, J.V. Hulse, A. Folleco, An empirical study of the classification performance of learners on imbalanced and noisy software quality data, Information Sciences, in press. http://dx.doi.org/10. 1016/j.ins.2010.12.016.
50. D. Sheskin Handbook of Parametric and Nonparametric Statistical Procedures 2011 Chapman & Hall/CRC
51. Y. Sun, A.K.C. Wong, and M.S. Kamel Classification of imbalanced data: a review International Journal of Pattern Recognition and Artificial Intelligence 23 2009 687 719
52. D. Wang, and T.S. Dillon Extraction of classification rules characterized by ellipsoidal regions using soft-computing techniques International Journal of Systems Science 37 2006 969 980
53. G.M. Weiss Mining with rarity: a unifying framework SIGKDD Explorations 6 2004 7 19
54. D.L. Wilson Asymptotic properties of nearest neighbor rules using edited data IEEE Transactions on Systems, Man and Cybernetics 2 1972 408 421
55. M.L. Wong, and K.S. Leung Data Mining Using Grammar-Based Genetic Programming and Applications 2000 Kluwer Academic Publishers
56. Q. Yang, and X. Wu 10 challenging problems in data mining research International Journal of Information Technology & Decision Making 5 2006 597 604
57. B. Zadrozny, C. Elkan, Learning and making decisions when costs and probabilities are both unknown, in: Proceedings of the 7th International Conference on Knowledge Discovery and Data Mining (KDD'01), 2001, pp. 204-213.