A method for attribute selection in inductive learning systems

Author

Baim, Paul W.

Author_Institution

Atlantic Aerosp. Electron. Corp., Waltham, MA, USA

Volume

10

Issue

6

fYear

1988

fDate

11/1/1988 12:00:00 AM

Firstpage

888

Lastpage

896

Abstract

A computable measure was developed that can be used to discriminate between attributes on the basis of their potential value in the formation of decision rules by the inductive learning process. This relevance measure is the product of extensions to an information-theoretic foundation that address the particular characteristics of a class of inductive learning algorithms. The measure is also conceptually compatible with approaches from pattern recognition. It is described in the context of a generalized model of the expertise development process, and an experiment is presented in which a significant reduction in the number of attributes to be considered was achieved for a complex medical domain

Keywords

artificial intelligence; knowledge acquisition; learning systems; pattern recognition; attribute selection; decision rules; inductive learning systems; knowledge acquisition; machine learning; pattern recognition; rule based systems; Context modeling; Diseases; Expert systems; Learning systems; Machine learning; Machine learning algorithms; Medical diagnostic imaging; Modems; Particle measurements; Pattern recognition;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.9110

Filename

9110