Recognition of E-Shopper Behavior Pattern Based on a Dynamic Architecture Neural Networks and Genetic Algorithm

Abstract

With the rapid development of online shopping, the ability to intelligently collect and analyze information about E-shoppers has become a key source of competitive advantage for firms. This paper presents an optimal algorithm of modeling dynamic architecture for artificial neural networks (ANN) and a novel machine-learning algorithm for extracting rules from databases via using genetic algorithm. Classical architecture of ANN is arbitrary. Before training ANN, the number of hidden layers and hidden nodes has already been fixed. In the dynamic architecture, the number of hidden layers and the number of hidden nodes are sequentially and dynamically generated until a level of performance accuracy is reached. In addition, in this paper, a new genetic algorithm is proposed, which does not need the computational complexity. The genetic algorithm is used to find the optimal values of input attributes(chromosome), Xm, which maximizes output function phiK of output node k. The optimal chromosome is decoded and used to obtain a rule belonging to class k. The better result is achieved by applying the two new algorithms to a given database for customers buying computer.