NEFCLASS-X — a Soft Computing Tool to Build Readable Fuzzy Classifiers

Neuro-fuzzy classification systems offer a means of obtaining fuzzy classification rules by a learning algorithm. Although it is usually no problem to find a suitable fuzzy classifier by learning from data, it can, however, be hard to obtain a classifier that can be interpreted conveniently. There is usually a trade-off between accuracy and readability. This paper discusses NEFCLASS — a neuro-fuzzy approach for classification problems — and its implementation NEFCLASS-X. It is shown how a readable fuzzy classifier can be obtained by a learning process and how interactive strategies for pruning rules and variables from a trained classifier can enhance its interpretability.