INDUCTION OF KNOWLEDGE USING EVOLUTIONARY ROUGH SET THEORY

Rough set theory, which emerged about 20 years ago, is nowadays a rapidly developing branch of artificial intelligence and soft computing. We will use rough set theory for modeling a classification system and applying genetic operations to a population of trees, which will be induced randomly or via the C4.5 method from the decision table with different pruning-constant settings. At first glance, the methodologies we discuss, namely, rough set theory and genetic programming, have nothing in common. However, it is interesting to try to incorporate these approaches into the hybrid system. The challenge is to get as much as possible from this association.

[1]  Shusaku Tsumoto,et al.  Discovery of Approximate Medical Knowledge Based on Rough Set Model , 1998, PKDD.

[2]  Zdzislaw Pawlak,et al.  Rough classification , 1984, Int. J. Hum. Comput. Stud..

[3]  Ning Zhong,et al.  Rule Discovery by Probabilistic Rough Induction , 2000 .

[4]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[5]  C. J. V. Rijsbergen,et al.  Rough Sets, Fuzzy Sets and Knowledge Discovery , 1994, Workshops in Computing.

[6]  Hirotaka Nakayama,et al.  Rule extraction based on rough set theory and its application to medical data analysis , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[7]  J. K. Kinnear,et al.  Advances in Genetic Programming , 1994 .

[8]  John R. Koza.  Genetic Programming III - Darwinian Invention and Problem Solving , 1999, Evolutionary Computation.

[9]  Andrzej Czyzewski,et al.  Rough Set Methods and Applications , 1998, Rough Sets and Current Trends in Computing.

[10]  S. Egawa,et al.  A study of pretreatment nomograms to predict pathological stage and biochemical recurrence after radical prostatectomy for clinically resectable prostate cancer in Japanese men. , 2001, Japanese journal of clinical oncology.

[11]  William B. Langdon,et al.  Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! , 1998 .

[12]  Lech Polkowski,et al.  Rough Sets in Knowledge Discovery 2 , 1998 .