A data mining approach for fuzzy classification rule generation

Author

Wang, Dianhui ; Dillon, Tharam S. ; Chang, Elizabeth J.

Author_Institution

Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China

fYear

2001

fDate

25-28 July 2001

Firstpage

2960

Abstract

This paper aims at developing a data mining approach for fuzzy classification rule generation. A regularization theory based theoretical framework for refining fuzzy classification rules is proposed. Our fuzzy rule induction methodology has four phases, namely: (1) ellipsoidal crisp rule generation with membership function assignment, (2) generic hyperbox fuzzy rule (GHR) derivation, (3) refinement of the GHR using a regularization model, and (4) simplification of the GHR by selecting an informative subset of premises out of the initial set

Keywords

data mining; fuzzy logic; fuzzy set theory; pattern classification; data mining; ellipsoidal crisp rule generation; fuzzy classification; fuzzy logic; fuzzy membership functions; fuzzy model; fuzzy rule induction; fuzzy rules; fuzzy sets; generic hyperbox fuzzy rule derivation; linguistic interpretability; membership function assignment; refining fuzzy classification; regularization theory; rule generation; Data mining; Decision making; Fuzzy logic; Fuzzy sets; Induction generators; Input variables; Nonlinear control systems; Pattern recognition; Software engineering; Strontium;

fLanguage

English

Publisher

ieee

Conference_Titel

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.943698

Filename

943698