1. Filter unsupervised spectral feature selection method for mixed data based on a new feature correlation measure.
- Author
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Solorio-Fernández, Saúl, Carrasco-Ochoa, J. Ariel, and Martínez-Trinidad, José Fco.
- Subjects
- *
FEATURE selection , *EVALUATION methodology - Abstract
In recent years, Unsupervised Feature Selection (UFS) methods have attracted considerable interest in different research areas due to their wide application in problems where unlabeled data appears. Nevertheless, few UFS methods can process datasets described by both numerical and non-numerical features (mixed data). This paper introduces a new filter UFS method based on a new feature correlation measure for mixed data to select a relevant and non-redundant feature subset. The proposed method addresses the feature selection problem in two stages through a strategy that combines Spectral Feature Selection to identify relevant features and a Pair-wise Redundancy Analysis to remove those features with a high correlation with others. According to our experiments, the proposed feature selection method based on the introduced feature correlation measure provides better quality results than previous filter UFS methods for mixed data reported in the literature. • A filter Unsupervised Feature Selection method for mixed data is presented. • A new feature correlation measure for mixed data is introduced. • Spectral Feature Selection and Pair-wise Redundancy Analysis were applied. • A comprehensive evaluation of the proposed method is provided. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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