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, if 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.