Genetic algorithms as a tool for restructuring feature space representations

Author

Vafaie, Haleh ; De Jong, Kenneth

Author_Institution

Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA

fYear

1995

fDate

5-8 Nov 1995

Firstpage

8

Lastpage

11

Abstract

This paper describes an approach being explored to improve the usefulness of machine learning techniques to classify complex, real world data. The approach involves the use of genetic algorithms as a “front end” to a traditional tree induction system (ID3) in order to find the best feature set to be used by the induction system. This approach has been implemented and tested on difficult texture classification problems. The results are encouraging and indicate significant advantages of the presented approach

Keywords

feature extraction; genetic algorithms; image classification; image texture; inference mechanisms; learning (artificial intelligence); ID3; feature space representations; genetic algorithms; image classification; image recognition; machine learning; texture classification problems; tree induction system; Algorithm design and analysis; Classification tree analysis; Computer science; Costs; Genetic algorithms; Image recognition; Machine learning; Manufacturing; Testing; Working environment noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1995. Proceedings., Seventh International Conference on

Conference_Location

Herndon, VA

ISSN

1082-3409

Print_ISBN

0-8186-7312-5

Type

conf

DOI

10.1109/TAI.1995.479372

Filename

479372