Genetic learner: Discretization and fuzzification of numerical attributes

摘要

Machine learning (ML) is a useful and productive component of data mining (DM). Given a large database, a learning algorithm induces a description of concepts (classes) which are immersed in a given problem area. The induction itself consists in searching usually a huge space of possible concept descriptions. There exist several paradigms for controlling this search. One of the promising and efficient paradigms are genetic algorithms (GAs). There have been done many research projects of incorporating genetic algorithms into the field of machine learning. This paper describes an efficient application of a GA in the attribute-based rule-inducing learning algorithm. Actually, a domain- independent GA has been integrated into the covering learning algorithm CN4, a large extension of the well--known algorithm CN2; the induction procedure of CN4 (beam search methodology) has been removed and the GA has been implanted into this shell.