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| 一种基于多进化神经网络的分类方法 |
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商琳1, 王金根2, 姚望舒1, 陈世福1
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1.南京大学,计算机软件新技术国家重点实验室,江苏,南京,210093;2.解放军焰兵学院,安徽,合肥,230031
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| 摘要: |
| 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(classification approach based on evolutionary neural networks).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 |
| 关键词: 进化计算 进化策略 神经网络 分类 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.60273033(国家自然科学基金);the NaturalScience Foundation of Jiangsu Province of China under Grant No.BK2004079(江苏省自然科学基金) |
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| A Classification Approach Based on Evolutionary Neural Networks |
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SHANG Lin,WANG Jin-Gen,YAO Wang-Shu,CHEN Shi-Fu
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| Abstract: |
| 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 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. |
| Key words: evolutionary computation evolutionary strategy neural network classification |
