Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/FE4E3C1D-E96A-413D-AE0C-6D063D64E575 | ||
| Year | 2007 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | A. Salappa, M. Doumpos and C. Zopounidis, "Feature selection algorithms in classification problems: an experimental evaluation," Optimizat. Meth. Software, vol. 22, no. 1, pp. 199-212, 2007. doi:10.1080/10556780600881910 https://doi.org/10.1080/10556780600881910 | ||
| Appears in Collections |
Feature selection (FS) is a significant topic for the development of efficient pattern recognition systems. FS refers to the selection of the most appropriate subset of features that describes (adequately) a given classification task. The objective of the present paper is to perform a thorough analysis of the performance and efficiency of feature selection algorithms (FSAs). The analysis covers a variety of important issues with respect to the functionality of FSAs, such as: (a) their ability to identify relevant features, (b) the performance of the classification models developed on a reduced set of features, (c) the reduction in the number of features and (d) the interactions between different FSAs with the techniques used to develop a classification model. The analysis considers a variety of FSAs and classification methods.
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