Learning Concept Classification Rules Using Genetic Algorithms

In this paper we explore the use of an adaptive search technique (genetic algorithms) to construct a system GABEL which continually learns and refines concept classification rules from its interaction with the environment. The performance of the system is measured on a set of concept learning problems and compared with the performance of two existing systems: ID5R and C4.5. Preliminary results support that, despite minimal system bias, GABIL is an effective concept learner and is quite competitive with ID5R and C4.5 as the target concept increases in complexity.

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