FF99: a novel fuzzy first-order logic learning system

Author

Leung, Kwong-Sak ; King, Irwin ; Tse, Ming-Fun

Author_Institution

Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

5

fYear

1999

fDate

1999

Firstpage

178

Abstract

This paper describes a novel learning system, named FF99, that learns fuzzy first-order logic concepts from various kinds of data. FF99 builds on the ideas from both fuzzy set theory and first-order logic. Object relationships are described using fuzzy relations based on which FF99 generates classification rules expressed in a restricted form of fuzzy first-order logic. This new system has been applied successfully to several tasks taken from the machine learning literature. We demonstrate its usefulness in the applications of data mining through several experiments

Keywords

fuzzy logic; fuzzy set theory; knowledge representation; learning (artificial intelligence); learning systems; data mining; first-order logic; fuzzy logic; fuzzy set theory; knowledge representation; learning system; machine learning; Costs; Data mining; Entropy; Fuzzy logic; Fuzzy set theory; Fuzzy systems; Iris; Learning systems; Machine learning; Relational databases;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.815544

Filename

815544