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Efficiently Handling Feature Redundancy in High-dimensional Data by Lei Yu; Huan Liu is a scholarly article available to read on EtoBox.

What is Efficiently Handling Feature Redundancy in High-dimensional Data about?

High-dimensional data poses a severe challenge for data mining. Feature selection is a frequently used technique in preprocessing high-dimensional data for successful data mining. Traditionally, feature selection is focused on removing irrelevant features. However, for high-dimensional data, removing redundant features is equally critical. In this paper, we provide a study of feature redundancy in high-dimensional data and propose a novel correlation-based approach to feature selection within the filter model. The extensive empirical study using real-world data shows that the proposed approach is efficient and effective in removing redundant and irrelevant features.

Author

Lei Yu; Huan Liu

Publisher

ACM

Published

2003

Language

EN