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PG-HMI:一种基于互信息的特征选择方法 被引量:6

PG-HMI:Mutual Information Based Feature Selection Method
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摘要 传统的基于样本的互信息估计方法不能直接处理离散、连续属性混合的情况.本文给出一种能够直接处理混合属性的互信息估计方法(PG 法).为了更好地考虑属性之间的关联,提出名为 HMI 的特征选择准则.结合PG 互信息估计方法和 HMI 特征选择准则,给出一种新的特征选择方法(PG-HMI).实验结果验证 PG 互信息估计法的合理性及 PG-HMI 特征选择方法的有效性. Conventional sample-based mutual information estimation methods can't handle the mixed features directly that include both numeric attributes and nominal attributes. A Parzen window based general mutual information calculation method, PG method, is proposed in this paper, which could deal with the mixed attributes directly. A criterion named hybrid mutual information (HMI) is presented. Based on PG mutual information estimation method and HMI feature selection criterion, a feature selection algorithm (PG-HMI) is proposed. Experimental results show the correctness of PG and the effectiveness of PG-HMI.
出处 《模式识别与人工智能》 EI CSCD 北大核心 2007年第1期55-63,共9页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金重大项目(No.50595414) 国家重点基础研究发展规划项目(No.2004CB217904) 国家自然科学基金项目(No.50107005) 新世纪优秀人才支持计划项目
关键词 特征选择 互信息 混合互信息(HMI) 分类器 数据挖掘 Feature Selection, Mutual Information, Hybrid Mutual Information (HMI), Classifier,Data Mining
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参考文献14

  • 1Piramuthu S. Evaluating Feature Selection Methods for Learning in Data Mining Applications//Proc of the 31st Hawaii International Conference on System Sciences. Kohala Coast, USA,1998, V: 294-301
  • 2HanJ KamberM.数据挖掘:概念与技术[M].北京:机械工业出版社,2001..
  • 3Battiti R. Using Mutual Information for Selecting Features in Supervised Neural Net Learning. IEEE Trans on Neural Networks, 1994, 5(4): 537-550
  • 4Kwak N, Choi C H. input Feature Selection for Classification Problems. IEEE Trans on Neural Networks, 2002, 13(1): 143-159
  • 5Kwak N, Choi C H. Input Feature Selection by Mutual Informarion Based on Parzen Window. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(12): 1667-1671
  • 6Peng Hanchuan, Long Fuhui, Ding C. Feature Selection Based on Mutual Information Criteria of Max-Dependency, Max-Relevance and Min-Redundancy. IEEE Trans on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238
  • 7Chow T W S, Huang D. Estimating Optimal Feature Subsets Using Efficient Estimation of High-Dimensional Mutual Information. IEEE Trans on Neural Networks, 2005, 16(1): 213-224
  • 8Quinlan J R. CA. 5 .. Programs for Machine Learning. San Mateo,USA: Morgan Kaufmann, 1993
  • 9Shannon C E, Weaver W. The Mathematical Theory of Communication. Urbana, USA: University of Illinois Press, 1949
  • 10朱雪龙.应用信息论基础[M].清华大学出版社,2000..

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