Knowledge discovery with NEFCLASS

Author

Nauck, Detlef

Author_Institution

Intelligent Syst. Res. Group, BT&D Technol. Ltd., Ipswich, UK

Volume

1

fYear

2000

fDate

2000

Firstpage

158

Abstract

In order to use fuzzy systems for knowledge discovery, we need algorithms to induce comprehensible fuzzy systems from data. The comprehensibility of a fuzzy model is mainly determined by its number of rules and variables, but also by meaningful membership functions. We discuss the value of interpretable fuzzy models for intelligent data analysis and highlight some features of the free neuro-fuzzy software NEFCLASS (NEuro-Fuzzy CLASSification) that uses special techniques to induce interpretable fuzzy classifiers from data

Keywords

data analysis; data mining; fuzzy neural nets; fuzzy systems; pattern classification; public domain software; NEFCLASS; comprehensible fuzzy systems induction; free software; intelligent data analysis; interpretable fuzzy classifiers; interpretable fuzzy models; knowledge discovery; membership functions; model comprehensibility; neuro-fuzzy classification; neuro-fuzzy software; rules; variables; Data analysis; Function approximation; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Intelligent systems; Learning systems; Neural networks; Predictive models; World Wide Web;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on

Conference_Location

Brighton

Print_ISBN

0-7803-6400-7

Type

conf

DOI

10.1109/KES.2000.885782

Filename

885782