Abstract:
:Feature selection plays an important role in machine learning and data mining. In recent years, various feature measurements have been proposed to select significant features from high-dimensional datasets. However, most traditional feature selection methods will ignore some features which have strong classification ability as a group but are weak as individuals. To deal with this problem, we redefine the redundancy, interdependence, and independence of features by using neighborhood entropy. Then the neighborhood entropy-based feature contribution is proposed under the framework of cooperative game. The evaluative criteria of features can be formalized as the product of contribution and other classical feature measures. Finally, the proposed method is tested on several UCI datasets. The results show that neighborhood entropy-based cooperative game theory model (NECGT) yield better performance than classical ones.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Zeng K,She K,Niu Xdoi
10.1155/2014/479289subject
Has Abstractpub_date
2014-01-01 00:00:00pages
479289eissn
1687-5265issn
1687-5273journal_volume
2014pub_type
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