Extracting rules from trained neural network using GA for managing e-business
The ability to intelligently collect, manage and analyze information about customers and sellers is a key source of competitive advantage for an e-business. This ability provides an opportunity to deliver real time marketing or services that strengthen customer relationships. This also enables an organization to gather business intelligence about a customer that can be used for future planning and programs. This paper presents a new algorithm for extracting accurate and comprehensible rules from databases via trained artificial neural network ( ANN) using genetic algorithm (GA). The new algorithm does not depend on the ANN training algorithms also it does not modify the training results. The GA is used to find the optimal values of input attributes ( chromosome), X-m, which maximize the output function psi(k) of output node k. The function psi(k) = f(x(i), (WG1)(i,j,) (WG2)(j,k)) is nonlinear exponential function. Where (WG1)(i,j), (WG2) (j,k) are the weights groups between input and hidden nodes, and hidden and output nodes, respectively. The optimal chromosome is decoded and used to get a rule belongs to class(k). (C) 2003 Elsevier B.V. All rights reserved.
e-business artificial neural network genetic algorithms personalization online shopping rule extraction