Soft decision trees: A genetically optimized cluster oriented approach

Expert Systems with Applications

Volumn 36, Issue 1, 2009, Pages 551-563

  Shukla, Sanjay Kumar ^a   Tiwari, M K ^b  

^a NATIONAL INSTITUTE OF FOUNDRY AND FORGE TECHNOLOGY   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Decision trees; Fuzzy clustering; Genetic algorithm; Inconsistency index

Indexed keywords

ARTIFICIAL INTELLIGENCE; CASE BASED REASONING; CLUSTERING ALGORITHMS; DATABASE SYSTEMS; DECISION MAKING; DECISION THEORY; DECISION TREES; FLOW OF SOLIDS; FUZZY CLUSTERING; GRANULATION; GRAPH THEORY; KNOWLEDGE BASED SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MINING; NETWORK PROTOCOLS; NEURAL NETWORKS; OPTIMIZATION; ROUGH SET THEORY; SENSOR NETWORKS; SET THEORY;

C-FUZZY DECISION TREES; DATA VALUES; FUZZY CLUSTERING ALGORITHMS; GENETIC ALGORITHM; INCONSISTENCY INDEX; INFORMATION GRANULES; LARGE DATABASES; MACHINE-LEARNING; MINING TASKS; NEW TOOLS; NOVEL METHODOLOGY; OBJECTIVE FUNCTION; PROPOSED ARCHITECTURES; RULE INDUCTION; SOFT CLUSTERING; SOFT DECISIONS; TREE STRUCTURES; VITAL INFORMATION;

TREES (MATHEMATICS);

EID: 53849085185     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.09.065     Document Type: Article

References (25)

1. Alexander W.P., and Grimshaw S. Treed regression. Journal of Computational Graphical Statistics 5 (1996) 156-175
2. Back T. Evolutionary algorithms in theory and practice (1996), Oxford University Press
3. Bezdek J.C. Pattern recognition with fuzzy objective functions (1981), Plenum, New York
4. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and regression trees (1984), Wadsworth, Belmont, CA
5. Cantu-Paz, E., & Kamath, C. (2000). Using evolutionary algorithm to induce oblique decision trees. In Proceedings of the genetic evolutionary computation conference (pp. 1053-1060).
6. Chen M.S., Han J., and Yu P.S. Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8 (1996) 866-883
7. Dennis Jr. J.E. Numerical methods for unconstrained optimization and nonlinear equations (1983), Prentice-Hall, Englewood Cliffs, NJ
8. Dennis J.E., and Woods Jr. D.J. Optimization on microcomputers: The Nelder-Mead simplex algorithm. In: Wouk A. (Ed). New computing environments: Microcomputers in large-scale computing (1987), SIAM, Philadelphia 116-122
9. Dobra, A., & Gehrke, J. (2002). SECRET: A scalable linear regression tree algorithm. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery data mining.
10. Eggermont, J. (2002). Evolving fuzzy decision trees with genetic programming and clustering. In Proceedings of fifth European conference on genetic programming (Euro'02). LNCS (Vol. 2278).
11. Goldberg D. Genetic algorithms in search. Optimization and machine learning (1989), Addison-Wesley
12. Goldberg D.E., and Deb K. A comparative analysis of selection schemes used in GA's. In: Rawlins G.J.E. (Ed). Foundations of genetic algorithms (1991), Morgan Kaufmann 69-73
13. Han J., and Kamber M. Data mining: Concepts and techniques (2001), Morgan Kaufman Publishers
14. Hathaway R.J., and Bezdek J.C. Optimization of clustering criteria by reformulation. IEEE Transactions Fuzzy Systems 3 2 (1995) 241-245
15. Maulik U., and Bandyopadhyay S. Fuzzy partitioning using a real-coded variable length genetic algorithm for pixel classification. IEEE Transactions on Geosciences and Remote Sensing 41 5 (2003)
16. Merz, C. J., & Murphy, P. M. (1996). UCI repository for machine learning data-bases. Department of Information and Computer Science, University of California, Irvine, CA. [Online]. Available: http://www.ics.vci.edu/~mlearn/MLRepository.html.
17. Pedrycz W., and Sosnowski Z.A. Desigening decision trees with the use of fuzzy granulation. IEEE Transactions on System, Man, and Cybernetics, Part A - Systems and Humans 30 2 (2000) 151-159
18. Pedrycz W., and Sosnowski A. C-fuzzy decision trees. IEEE Transactions on Systems, Man, and Cybernetics 34 4 (2005) 498-511
19. Quinlan J.R. C4.5: Programs for machine learning (1993), Morgan Kaufmann, San Francisco, CA
20. Quinlann J.R. Induction of decision trees. Machine Learning 1 (1986) 81-106
21. Mendes, Roberto R.F., Voznika, Fabricio de B., Freitas, Alex A., & Nievola, Julio C. (2001). Discovering fuzzy classification rules with genetic programming and co-evolution. In Proceedings of fifth European conference on principles of data mining and knowledge discovery. LNCS (Vol. 2168, pp. 314-325).
22. Sorensen K., and Janssens G.K. Data mining with genetic algorithm on binary trees. European Journal of Operational Research 151 (2003) 253-264
23. Tseng L.Y., and Yang S.B. A genetic approach to the automatic clustering problem. Patten Recognition 34 (2001) 415-424
24. Weber, R. (1992). Fuzzy ID3: A class of methods for automatic knowledge acquisition. In Proceedings of the second international conference on fuzzy logic neural networks (pp. 265-268).
25. Yildiz O.T., and Alpaydin E. Omnivariate decision trees. IEEE Transactions on Neural Networks 12 6 (2001) 1539-1546