Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution

Lei Yu, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1326 Scopus citations

Abstract

Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this work, we introduce a novel concept, predominant correlation, and propose a fast filter method which can identify relevant features as well as redundancy among relevant features without pairwise correlation analysis. The efficiency and effectiveness of our method is demonstrated through extensive comparisons with other methods using real-world data of high dimensionality.

Original languageEnglish (US)
Title of host publicationProceedings, Twentieth International Conference on Machine Learning
EditorsT. Fawcett, N. Mishra
Pages856-863
Number of pages8
StatePublished - Dec 1 2003
EventProceedings, Twentieth International Conference on Machine Learning - Washington, DC, United States
Duration: Aug 21 2003Aug 24 2003

Publication series

NameProceedings, Twentieth International Conference on Machine Learning
Volume2

Other

OtherProceedings, Twentieth International Conference on Machine Learning
CountryUnited States
CityWashington, DC
Period8/21/038/24/03

    Fingerprint

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yu, L., & Liu, H. (2003). Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution. In T. Fawcett, & N. Mishra (Eds.), Proceedings, Twentieth International Conference on Machine Learning (pp. 856-863). (Proceedings, Twentieth International Conference on Machine Learning; Vol. 2).