Fuzzy decision tree construction with gene expression programming

Proceedings of 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2010

Volumn , Issue , 2010, Pages 244-248

  Weihong, Wang ^a   Wei, Ruan ^a   Qu, Li ^b  

^a ZHEJIANG UNIVERSITY OF TECHNOLOGY   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

Decision tree; Fuzzy information processing; Gene expression programming

Indexed keywords

CLASSIFICATION ACCURACY; DECISION TREE TECHNIQUES; EVOLUTIONARY COMPUTATION TECHNIQUES; FUZZY ATTRIBUTES; FUZZY DECISION TREES; FUZZY INFORMATION; FUZZY INFORMATION PROCESSING; GENE EXPRESSION PROGRAMMING;

DATA PROCESSING; DECISION TREES; INTELLIGENT SYSTEMS; KNOWLEDGE ENGINEERING;

GENE EXPRESSION;

EID: 79551510440     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISKE.2010.5680877     Document Type: Conference Paper

References (16)

1. J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-106, 1986.
2. Quinlan JR, C4.5: Programs for machine learning. Morgan Kaufmann Publishers, San Mateo, 1993
3. Breiman L, Friedman JH, Olshen RA, Stone CJClassification and regression trees. Wadsworth, Belmont ,1984
4. J. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, CA, 2006.
5. J. R. Koza, Genetic Programming. Cambridge, MA: MIT Press, 1992.
6. A. Papagelis, D. Kalles, Breeding decision trees using evolutionary techniques. Proc. 18th Int. Conf. on Machine Learning (ICML-2001), 393-400. Morgan Kaufmann, 2001.
7. C.Ferreira, Gene Expression Programming: a New Adaptive Algorithm for Solving Problems.Complex Systems, 13, 2 (2001), 87-129.
8. Candida Ferreira. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, 2nd Edition Springer, 2006.
9. Zuo Jie, Tang Changjie, Zhang Tianqing. Mining predicate association rule by gene expression programming, Proc of the 3rd International Conference onWeb Age Information Management (WAIM 2002) 92-103, 2002.
10. Chi Zhou, Weimin Xiao, Thomas M. Tirpak, and Peter C. Nelson. Evolving Accurate and Compact Classification Rules with Gene Expression Programming. IEEE Transactions on Evolutionary Computation, 7(6):519-531, December 2003.
11. O. Cordon, F. A. C. Gomide, F. Herrera, F. Hoffmann, and L. Magdalena. Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets and Systems, 141(1):5-31, 2004
12. H. Ishibuchi, K. Nozaki, and N. Yamamoto. Selecting fuzzy if-then rules for classification problem using genetic algorithm. IEEE Trans.Fuzzy system, 3(3):260-270, 1995.
13. M.H. Marghny and I.E. El-Semman. Extracting Logical ClassificationRules with Gene Expression Programming: Microarray Case Study [A]. Proceedings of the International Conference on Artificial Intelligence and Machine Learning. Egypt, 2005
14. S.Luke, ECJ 18 : An Evolutionary Computation research system in Java. Available at http://www.cs.umd.edu/projects /plus/ec/ecj/ (2009)
15. I. H. Witten, Eibe Frank, Data Mining Practical Machine Learning Tools and Techniques with java Implementations, Morgan Kaufmann Publishers, USA, 2000.
16. C.Blake, E. Keogh, C. J. Merz, UCI. repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA.