¹Business School, Huaihai Institute of Technology, Lianyungang, 222000, China
²Library, Huaihai Institute of Technology, Lianyungang, 222000, China
³School of Management, Harbin Institute of Technology, Harbin, 150001, China
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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 a 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. 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 presented, which does not need the computational complexity. The genetic algorithm is used to find the optimal values of input attributes Xm , which maximizes output function φk 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 e-shoppers buying computer.

Index Terms—e-shopper, neural network, genetic algorithm, rule extraction

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Cite: Chong Wang, Jian Liu, Yanqing Wang, "Research on Classification of E-shopper Based on Neural Networks and Genetic Algorithm," Journal of Computers vol. 6, no. 4, pp. 627-634, 2011.