Rule extraction based on rough set theory combined with genetic programming and its application to medical data analysis

• Published in: The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001 pp. 385 - 390
• Main Authors: Hassan, Y., Tazaki, E.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2001
• Subjects: Artificial intelligence; Biomedical engineering; Control systems; Data mining; Genetic engineering; Genetic programming; Information systems; Medical control systems; Set theory
• ISBN: 9781740520614; 1740520610
• DOI: 10.1109/ANZIIS.2001.974109

A methodology for using rough sets for preference modeling in decision problems is presented in this paper, where we introduce a new approach for deriving knowledge rules from medical databases based on rough sets combined with genetic programming. Genetic programming is one of the newest techniques in applications of artificial intelligence. Rough set theory (Z. Pawluk, 1982), is nowadays rapidly developing branch of artificial intelligence and soft computing. At first glance, the two methodologies have nothing in common. Rough sets construct the representation of knowledge in terms of attributes, semantic decision rules, etc. On the other hand, genetic programming attempts to automatically create computer programs from a high-level statement of the problem requirements. However, in spite of these differences, it is interesting to try to incorporate both approaches into a combined system. The challenge is to get as much as possible from this association.