MIKM: A mutual information-based K-medoids approach for feature selection

Jung Yi Jiang, Yao Lung Su, Shie Jue Lee

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

We propose a mutual information-based K-medoids approach (MIKM) for unsupervised and supervised feature selection, MIKM adopts mutual information (MI) to measure similarity between two features and applies K-medoids clustering to find representatives of feature clusters. The method partitions the original feature set into some distinct subsets or clusters such that the features within a cluster are highly similar to each other while those in different clusters are dissimilar, Each obtained representative is one of the original features. Consequently, The obtained representatives form a subset of the original features. Experimental results show that our proposed method can work more effectively than other methods.

原文English
主出版物標題Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
頁面102-107
頁數6
DOIs
出版狀態Published - 2011 十一月 7
事件2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
持續時間: 2011 七月 102011 七月 13

出版系列

名字Proceedings - International Conference on Machine Learning and Cybernetics
1
ISSN(列印)2160-133X
ISSN(電子)2160-1348

Other

Other2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
國家/地區China
城市Guilin, Guangxi
期間11-07-1011-07-13

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 計算機理論與數學
  • 電腦網路與通信
  • 人機介面

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