Genetic Algorithms as a Tool for Feature Selection in Machine Learning

This paper describes an approach being explored to improve the usefulness of machine learning techniques for generating classification rules for complex, real world data. The approach involves the use of genetic algorithms as a "front end" to traditional rule induction systems in order to identify and select the best subset of features to be used by the rule induction system. This approach has been implemented and tested on difficult texture classification problems. The results are encouraging and indicate significant advantages to the presented approach in this domain.

• Type: Conference
• Year: 1992
• Where: ICTAI
• Authors: Haleh Vafaie, Kenneth A. De Jong
• Keywords: Artificial Intelligence, ICTAI 1992, Machine Learning Techniques, Rule Induction System, Traditional Rule Induction