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A Method for Attribute Selection in Inductive Learning Systems
November 1988 (vol. 10 no. 6)
pp. 888-896

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.

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Index Terms:
machine learning; rule based systems; knowledge acquisition; attribute selection; inductive learning systems; decision rules; pattern recognition; artificial intelligence; knowledge acquisition; learning systems; pattern recognition
Citation:
P.W. Baim, "A Method for Attribute Selection in Inductive Learning Systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 888-896, Nov. 1988, doi:10.1109/34.9110
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