document: Efficiently handling feature redundancy in high-dimensional data High-dimensional data poses a severe challenge for data mining . Feature selection is a frequently used technique in pre-processing 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 .
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