Title :
Knowledge discovery with NEFCLASS
Author_Institution :
Intelligent Syst. Res. Group, BT&D Technol. Ltd., Ipswich, UK
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;
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
DOI :
10.1109/KES.2000.885782
