Journal
PATTERN RECOGNITION LETTERS
Volume 138, Issue -, Pages 321-328Publisher
ELSEVIER
DOI: 10.1016/j.patrec.2020.07.039
Keywords
Supervised feature selection; Mixed data; Filter feature subset selection; Redundancy analysis
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Funding
- National Council of Science and Technology of Mexico (CONACyT) [224490]
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Spectral analysis and Information-theory are two powerful and successful frameworks for feature selection in supervised classification problems. However, most of the methods developed under these frameworks have been introduced for handling exclusively numerical or non- numerical data. In this paper, we propose a supervised filter feature selection method that combines Spectral Feature Selection and Information-theory based redundancy analysis for selecting relevant and non-redundant features in supervised mixed datasets; i.e., datasets where the objects are described simultaneously by both, numerical and non-numerical features. To demonstrate the effectiveness of our proposed supervised filter feature selection method, we conducted several experiments on 40 public real-world datasets. Additionally, we compare our method against relevant state-of-the-art supervised filter methods for numerical, nonnumerical, and mixed data. From this comparison, our method, in general, obtains better results than the results obtained by the other evaluated filter feature selection methods. (C) 2020 Elsevier B.V. All rights reserved.
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