Decision Rule Extraction and Reduction Based on Grey Lattice Classification

Authors:
Daisuke Yamaguchi;Guo-Dong Li;Takahiro Akabane
Affiliations:
Kanagawa University, Japan;Teikyo University, Japan;Teikyo University, Japan
Venue:
ICMLA '05 Proceedings of the Fourth International Conference on Machine Learning and Applications
Year:
2005

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Abstract

This paper proposes a decision rule of extraction and reduction that is based on grey lattice classification. This proposal method becomes from joining between rough set theory and grey theory as an approximation algorithm. Grey lattice operations are defined by combining interval grey number in grey theory with interval lattice operations in interval algebra. By defining the equivalents in interval grey number, given data space is correspondent to equivalents of rough set. This proposalmethod classifies each data set into 3-patterns from given training samples, as existing possibility class, newly made possibility class and existing necessity class. As given examples which require only necessity class, decision rule is simplified by a reduction procedure.