Adaptive resolution min-max classifier
Antonello Rizzi, Fabio Massimo Frattale Mascioli, Giovanni Martinelli · 2002
This paper presents a new neuro-fuzzy classifier, inspired by the Simpson's (1992, 1993) min-max model. By relying on a constructive approach, it overcomes some undesired properties of the original min-max algorithm. In particular, training result does 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. Consequently, the new algorithm yields less complex networks, thus increasing the generalization capability in accordance with learning theory paradigms. Several tests are presented for illustration.