An Effective Intelligent Self-Construction Multilayer Perceptron Neural Network

Abstract

A new classifier algorithm based on Multilayer Perceptron Neural Network (MPNN), Apriori association rules, and Particle Swarm Optimization (PSO) models is proposed. It provides a comprehensive analytic method for establishing an Artificial Neural Network (ANN) with self-organizing architecture by finding an optimal number of hidden layers and their neurons, less number of effective features of data set, and better topology for internal connections. The performance of the proposed algorithm is evaluated using a number of benchmark data sets including Breast Cancer, Iris, and Yeast. Experimental results demonstrate the effectiveness and the notability of the proposed algorithm comparing with recently existed ANN learning and classification algorithms.