Abstract
Abstract Feature selection has been a fruitful field of research and it is undoubtedly important. However, a statement like “the best feature selection method” simply does not exist in general, making it difficult for users to select one method over another. For this reason, the objective of this chapter is to perform a critical review of state-of-the-art feature selection methods. The chapter starts with the description of the existing reviews (Section 3.1). Then, Section 3.2 depicts the methods and data involved in the experiments of this chapter and Section 3.3 shows the results obtained. In Section 3.4 we present several cases of study, aiming at deciding between methods that showed similar behaviors. Finally, Section 3.5 analyzes and discusses the findings of the experimental study and Section 3.6 summarizes this chapter.
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© 2015 Springer International Publishing Switzerland
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Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A. (2015). A Critical Review of Feature Selection Methods. In: Feature Selection for High-Dimensional Data. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-21858-8_3
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DOI: https://doi.org/10.1007/978-3-319-21858-8_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21857-1
Online ISBN: 978-3-319-21858-8
eBook Packages: Computer ScienceComputer Science (R0)