A Self-Organizing Fuzzy Polynomial Neural Network - Multistage Classifier

Abstract

A fuzzy polynomial neural network multistage classifier (FPNN-MC) is suggested in this paper, suitable for handling complex classification problems with large feature spaces. The multilayered FPNN-MC structure is developed in a self-organizing way, using a structure learning procedure. The network's neurons are realized through fuzzy rule-based TSK systems, considered as generic fuzzy neuron classifiers (FNC's). Parent FNC's are combined to develop new higher-level descendant classifiers at the subsequent layer. Hence, sequential multistage decision is implemented, leading to improved classification results. To exploit the information acquired by FNC's at each layer and achieve an effective data flow, a fusion scheme is developed associated with a data reduction mechanism. Upon termination of the structure building, parameter learning is carried out using a genetic algorithm platform. A remarkable asset of the approach is that it resolves the feature selection task, providing the most relevant features of a problem. Simulation results on a well known classification problem indicate the efficiency of the proposed model