Learning in neuro-fuzzy systems with symbolic attributes and missing values

Abstract

Neuro-fuzzy classification approaches aim at creating fuzzy classification rules from data by using learning techniques derived from neural networks. NEFCLASS is able to learn fuzzy rules and fuzzy sets by simple heuristics. The aim of NEFCLASS is to quickly create interpretable fuzzy classifiers. Most neuro-fuzzy approaches can only deal with numerical attributes and cannot handle missing values. The authors present recent advances in the learning algorithms of NEFCLASS that address those problems.