Accession Number : ADA294470

Title :   Learning Concept Classification Rules using Genetic Algorithms,

Corporate Author : GEORGE MASON UNIV FAIRFAX VA

Personal Author(s) : DeJong, Kenneth A. ; Spears, William M.

PDF Url : ADA294470

Report Date : 1990

Pagination or Media Count : 6

Abstract : In this paper we explore the use of an adaptive search technique (genetic algorithms) to construct a system GABIL 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. (AN)

Descriptors :   *ALGORITHMS, *RULE BASED SYSTEMS, *LEARNING, OPTIMIZATION, COMPARISON, SEMANTICS, PROBLEM SOLVING, DECISION THEORY, SEARCHING, CLASSIFICATION, DATA ACQUISITION, ADAPTIVE SYSTEMS, PATTERN RECOGNITION, SYSTEMS ANALYSIS, BIAS, SYNTAX.

Subject Categories : Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE