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Feature Selection for High-Dimensional Data: A fast Correlation- based filter SolutionLei of Computer Science & Engineering, Arizona State University, Tempe, AZ 85287-5406, USAA bstractFeature Selection , as a preprocessing step tomachine learning, is effective in reducing di-mensionality, removing irrelevant data, in-creasing learning accuracy, and improvingresult comprehensibility. However, the re-cent increase of dimensionality of data posesa severe challenge to many existing featureselection methods with respect to efficiencyand effectiveness. In this work, we intro-duce a novel concept, predominant correla-tion, and propose a fast filter method whichcan identify relevant features as well as re-dundancy among relevant features withoutpairwise correlation analysis. The efficiencyand effectiveness of our method is demon-strated through extensive comparisons withother methods using real-world data of IntroductionFeature Selection is frequently used as a preprocessingstep to machine learning.
A Fast Correlation-Based Filter Solution Lei Yu leiyu@asu.edu Huan Liu hliu@asu.edu Department of Computer Science & Engineering, Arizona State University, Tempe, AZ 85287-5406, USA Abstract Feature selection, as a preprocessing step to machine learning, is efiective in reducing di-mensionality, removing irrelevant data, in-
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