International Journal of Computational Intelligence Research (IJCIR)

Volume 2, Number 1 (2006)


A classification approach based on evolutionary neural networks

Zhong Jing1,2, Fu Yan2, Zhou Jun-lin2
Department of Mathematics and Computer Science, Chongqing Three Gorges University, Shalong Road 780, Wanzhou, Chongqing 404000, China 

2Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Jianshe North Road 4, Chendu, 610054, China


Classification is important in data mining and machine learning. In this paper, a classification approach based on evolutionary neural networks (CABEN) is presented, which establishes classifiers by a group of three-layer feed-forward neural networks. The neural networks are trained by an improving algorithm synthesizing modified Evolutionary Strategy and Levenberg-Marquardt optimization method. The class label of the identifying data can first be evaluated by each neural network, and the final classification result is obtained according to the absolute-majority-voting rule. Experimental results show that the algorithm is effective for the classification, and has the better performance in classification precision, comparing with Bayesian and decision trees, especially for the complex classification problems with many classes.

classification, evolutionary neural networks, Levenberg-Marquardt, absolute-majority-voting.