全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

PGHMI:一种基于互信息的特征选择方法*

, PP. 55-63

Keywords: 特征选择,互信息,混合互信息(HMI),分类器,数据挖掘

Full-Text   Cite this paper   Add to My Lib

Abstract:

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.

References

[1]  Battiti R. Using Mutual Information for Selecting Features in Supervised Neural Net Learning. IEEE Trans on Neural Networks, 1994, 5(4): 537550
[2]  Kwak N, Choi C H. Input Feature Selection for Classification Problems. IEEE Trans on Neural Networks, 2002, 13(1): 143159
[3]  Kwak N, Choi C H. Input Feature Selection by Mutual Information Based on Parzen Window. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(12): 16671671
[4]  Peng 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
[5]  Chow 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
[6]  Quinlan J R. C4.5: Programs for Machine Learning. San Mateo, USA: Morgan Kaufmann, 1993
[7]  Shannon C E, Weaver W. The Mathematical Theory of Communication. Urbana, USA: University of Illinois Press, 1949
[8]  Zhu Xuelong. Fundamentals of Applied Information Theory. Beijing, China: Tsinghua University Press, 2000 (in Chinese) (朱雪龙.应用信息论基础.北京:清华大学出版社, 2000)
[9]  Parzen E. On Estimation of a Probability Density Function and Mode. Annals of Mathematical Statistics, 1962, 33(3): 10651076
[10]  Bian Zhaoqi, Zhang Xuegong. Pattern Recognition. Beijing, China: Tsinghua University Press, 2000 (in Chinese) (边肇祺,张学工.模式识别.北京:清华大学出版社, 2000)
[11]  Silverman B W. Density Estimation for Statistics and Data Analysis. London, UK: Chapman & Hall, 1986
[12]  Hettich S, Bay S D. The UCI KDD Archive[DB/OL]. [19990909]. http://kdd.ics.uci.edu
[13]  The University of Wakato. WEKA Software [CP/OL]. [20051020]. http://www.cs.waikato.ac.nz/~ml/weka
[14]  Piramuthu 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
[15]  Han J, Kamber M. Data Mining: Concepts and Techniques. New York, USA: Morgan Kaufman, 2000 (Han J, Kamber M. 数据挖掘:概念与技术.范 明,孟小峰,等,译.北京:机械工业出版社, 2001)

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133