Augmented Fuzzy Min-Max Neural Network Driven to Preprocessing Techniques and Space Search Optimization Algorithm

In this paper, an augmented fuzzy min-max neural network (AFMNN) with the preprocessing techniques and space search optimize algorithm (SSOA) is proposed. The purpose of this approach is to reduce the complexity of the hyperbox as well as to eliminate the hyperbox overlap problem. AFMNN consists of four stages, which are input layer, preprocessing layer, hyperbox generation layer and output layer. In preprocessing layer, important features are selected through information gain to eliminate the negative impact of redundant and irrelevant features on hyperbox construction. The hyperbox generation layer consists of two parts: hyperbox generation and hyperbox optimization. In this part, the hyperbox contraction process that causes data distortion is eliminated, and the minimum and maximum points of hyperboxes are optimized using a space search optimization algorithm (SSOA) to reduce overlap issues of hyperboxes. A series of experiments on benchmark datasets are considered to evaluate the performance of the AFMNN. A comparative analysis shows that the proposed AFMNN has good performance compared with when compared with state-of-art models reported in literature.