TPPFAM: Use of threshold and posterior probability for category reduction in fuzzy ARTMAP

The issue of category proliferation caused by the overlapping classes in fuzzy ARTMAP (FAM) is addressed in this paper. A new FAM-based neural architecture called TTPFAM is proposed, which can reduce category proliferation by performing a threshold filtering mechanism before a new category created during training, and improve the classification accuracy by combining prediction distributed by the dynamic Q-max rule and posterior probability estimated during testing. The TPPFAM can produce a small size of neural network architecture without degradation of the classification accuracy. The algorithm is evaluated in terms of the classification accuracy and the number of categories by experiments on both artificial and real data, and the results show that the performance of TPPFAM is better than that of the other models.