A feature construction approach for genetic iterative rule learning algorithm

Journal of Computer and System Sciences

Volumn 80, Issue 1, 2014, Pages 101-117

  García, David ^a   González, Antonio ^a   Pérez, Raúl ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Classification; Feature construction; Genetic fuzzy systems; Iterative learning approach

Indexed keywords

CLASSIFICATION (OF INFORMATION); ECONOMIC AND SOCIAL EFFECTS; FUZZY INFERENCE; FUZZY RULES; ITERATIVE METHODS; LEARNING SYSTEMS; NEONATAL MONITORING;

AMOUNT OF INFORMATION; FEATURE CONSTRUCTION; FUZZY RULE LEARNING; GENETIC FUZZY SYSTEMS; ITERATIVE LEARNING APPROACHES; ITERATIVE RULE LEARNING; LEARNING STRATEGY; MORE-GENERAL RULES;

LEARNING ALGORITHMS;

EID: 84884980599     PISSN: 00220000     EISSN: 10902724     Source Type: Journal    
DOI: 10.1016/j.jcss.2013.03.011     Document Type: Conference Paper

References (31)

1. E.H. Mamdani, and S. Assilian An experiment in linguistic synthesis with fuzzy logic controller Int. J. Man-Mach. Stud. 1 1975 1 13
2. C. Huang, and C. Moraga Extracting fuzzy if-then rules by using the information matrix technique J. Comput. System Sci. 70 1 2005 26 52
3. P. Langley, J.M. Zytkow, H.A. Simon, and G.L. Bradshaw The search for regularity: four aspects of scientific discovery Machine Learning 1986 Morgan Kaufman
4. S. Wrobel An algorithm for multi-relational discovery of subgroups Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery 1997 Springer Berlin
5. E. Bloedorn, R.S. Michalski, Data-driven constructive induction, in: IEEE Intelligent System, 1998, pp. 30-37.
6. M.G. Smith, and L. Bull Genetic programming with a genetic algorithm for feature construction and selection Genet. Program. Evol. Mach. 6 3 2005 265 281
7. S. Piramuthu, and R.T. Sikora Iterative feature construction for improving inductive learning algorithms Expert Syst. Appl. 36 2 2009 3401 3406
8. A. Lazaridis, T. Ganchev, I. Mporas, E. Dermatas, and N. Fakotakis Two-stage phone duration modeling with feature construction and feature vector extension for the needs of speech synthesis Comput. Speech Lang. 26 4 2012 274 292
9. O. Dor, and Y. Reich Strengthening learning algorithms by feature discovery Inform. Sci. 189 2012 176 190
10. A. González, and R. Pérez Improving the genetic algorithm of SLAVE Mathware Soft Comput. 16 2009 59 70
11. A. González, and R. Pérez A learning system of fuzzy control rules F. Herrera, J.L. Verdegay, Genetic Algorithms and Soft Computing 1996 Physica-Verlag Wurzburg 202 225
12. A. González, and R. Pérez SLAVE: a genetic learning system based on an iterative approach IEEE Trans. Fuzzy Syst. 7 2 1999 176 191
13. T. Mitchell Machine Learning 1997 MacGraw-Hill
14. D. García, A. González, R. Pérez, A two-step approach of feature construction for a genetic learning algorithm, in: Proc. 2011 IEEE International Conference on Fuzzy Systems, 2011, pp. 1255-1262.
15. D. García, A. González, R. Pérez, An iterative strategy for feature construction on a fuzzy rule-based learning algorithm, in: Proc. 11th International Conference on Intelligent Systems Design and Applications, 2011, pp. 1235-1240.
16. Y. Caises, E. Leyva, A. González, R. Pérez, A genetic learning of fuzzy relational rules, in: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, art. no. 5584718.
17. A. González, and R. Pérez Completeness and consistency conditions for learning fuzzy rules Fuzzy Sets and Systems 96 1998 37 51
18. A. González, R. Pérez, An analysis of the scalability of an embedded feature selection model for classification problems, in: Proceedings of the Information Processing and Management of Uncertainty on Knowledge-Based Systems IPMU 2006, 2006, pp. 1949-1956.
19. S. Kullback Information Theory and Statistics 1978 Peter Smith Gloucester, MA
20. L. Wehenkel, On uncertainty measures used for decision tree induction, in: Proc. of IPMU'96, 1996, pp. 413-418.
21. A. González A learning methodology in uncertain and imprecise environments Int. J. Intell. Syst. 19 1995 357 371
22. A. Asuncion, and D.J. Newman UCI Machine Learning Repository 2007 University of California, School of Information and Computer Science Irvine, CA available at: http://www.ics.uci.edu/~mlearn/MLRepository.html
23. L. Castillo, A. González, and R. Pérez Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm Fuzzy Sets and Systems 120 3 2001 309 321
24. E. Bernardó-Mansilla, X. Llorá, and J. Garrel XCS and gale: "A comparative study of two learning classifier" P. Lanzi, W. Stolzman, S. Wilson, Advances in Learning Classifier Systems Lecture Notes in Comput. Sci. vol. 2321 2002 Springer Heidelberg 115 132
25. A. González, and R. Pérez SLAVE: a genetic learning system based on an iterative approach IEEE Trans. Fuzzy Syst. 7 2 1999 176 191
26. O. Dunn Multiple comparisons among means J. Am. Stat. Assoc. 1961
27. J. Alcalá KEEL: A software tool to assess evolutionary algorithms to data mining problems Soft Comput. 13 3 2009 307 318
28. H. Ishibuchi Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems IEEE Trans. Syst. Man Cybern., Part B, Cybern. 29 5 1999 601 618
29. L. Sánchez, and I. Couso Combining GP operators with SA search to evolve fuzzy rule based classifiers Inform. Sci. 136 1-4 2001 175 192
30. J. Otero, and L. Sánchez Induction of descriptive fuzzy classifiers with the Logitboost algorithm Soft Comput. 10 9 2006 825 835
31. P. Domingos, and M. Pazzani On the optimality of the simple Bayesian classifier under zero-one loss Mach. Learn. 29 1997 103 137