Feature Selection via Maximizing Fuzzy Dependency

• Autorzy: Hu Q. , Zhu P. , Liu J. , Yang Y. , Yu D.
• Języki publikacji: EN

Abstrakty

EN

Feature selection is an important preprocessing step in pattern analysis and machine learning. The key issue in feature selection is to evaluate quality of candidate features. In this work, we introduce a weighted distance learning algorithm for feature selection via maximizing fuzzy dependency. We maximize fuzzy dependency between features and decision by distance learning and then evaluate the quality of features with the learned weight vector. The features deriving great weights are considered to be useful for classification learning. We test the proposed technique with some classical methods and the experimental results show the proposed algorithm is effective.

Słowa kluczowe

EN

feature selection; distance learning; fuzzy rough sets; fuzzy dependency

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2010
• Tom: Vol. 98, nr 2/3
• Strony: 167--181
• Opis fizyczny: Bibliogr. 31 poz., wykr.

Twórcy

autor

Hu Q.

autor

Zhu P.

autor

Liu J.

autor

Yang Y.

autor

Yu D.

• Harbin Institute of Technology, Harbin 150001, China,

Bibliografia

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