Automatic training of generalized Min-Max classifiers

Among fuzzy classifiers, Min-Max networks have the advantage to be trained in a constructive way, by a simple learning procedure. The classification strategy of Simpson's Min-Max classifier consists in covering the training data with hyperboxes constrained to have their boundary surfaces parallel to the coordinate axes of the chosen reference system. In order to obtain a more accurate data coverage, it is possible to adopt a new classification model which allows to arrange the hyperboxes orientation along any direction of the data space. The training algorithm is based on the ARC/PARC technique, which already yields better performances with respect to the original Simpson's algorithm. Although the most important feature of a classifier is its generalization capability, the effectiveness of a training procedure is strictly related to its automation degree. A low automation degree can be a serious drawback for a classification system, since it can prevent an unskilled user from successfully generate an acceptable model. From this point of view, a learning procedure should not depend on any critical parameter. The automation degree of the new classification system is evaluated in the paper.