A recursive algorithm for fuzzy min-max networks

An algorithm to train min-max neural models is proposed. It is based on the adaptive resolution classifier (ARC) technique, which overcomes some undesired properties of the original Simpson´s (1992) algorithm. In particular, training results do not depend on pattern presentation order and hyperbox expansion is not limited by a fixed maximum size, so that it is possible to have different covering resolutions. ARC generates the optimal min-max network by a succession of hyperbox cuts. The generalization capability of the ARC technique depends mostly on the adopted cutting strategy. A new recursive cutting procedure allows ARC technique to yield a better performance. Some real data benchmarks are considered for illustration