一种基于多进化神经网络的分类方法

A Classification Approach Based on Evolutionary Neural Networks

分类问题是目前数据挖掘和机器学习领域的重要内容.提出了一种基于多进化神经网络的分类方法CABEN(classificationapproachbasedonevolutionaryneuralnetworks).利用改进的进化策略和Levenberg-Marquardt方法对多个三层前馈神经网络同时进行训练.训练好各个分类模型以后,将待识别数据分别输入,最后根据绝对多数投票法决定最终分类结果.实验结果表明,该方法可以较好地进行数据分类,而且与传统的神经网络方法以及贝叶斯方法和决策树方法相比,在分类精度方面有明显的改善,体现出较好的稳定性和容错性,尤其适用于类别数较多且分类困难的复杂分类问题.

Classification is important in data mining and machine 1 earning. 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 sy nthesizing 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 CABEN is effective for the classification, and has the better performance in classification precision, stability and fault-tolerance comparing with the traditional neural network methods, Bayesian classifiers and decision trees, especially for the complex classification problems with many classes.