Learning Concept Classification Rules using Genetic Algorithms,

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)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1990
Accession Number
ADA294470

Entities

People

  • Kenneth A. Dejong
  • William M. Spears

Organizations

  • George Mason University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Classification
  • Command Control Communications And Computer Systems
  • Environment
  • Genetic Algorithms
  • Language
  • Learning
  • Machine Learning
  • Michigan
  • Military Research
  • Mutations
  • Side Effects
  • Standards
  • Test And Evaluation
  • Test Sets

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Biotechnology