Knowledge discovery with NEFCLASS
Detlef D. Nauck · 2002
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.