Institutional Repository
Technical University of Crete
Technical University of Crete
EN
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EL
| URI | http://purl.tuc.gr/dl/dias/FE4E3C1D-E96A-413D-AE0C-6D063D64E575 | - |
| Identifier | http://www.tandfonline.com/doi/full/10.1080/10556780600881910 | - |
| Identifier | https://doi.org/10.1080/10556780600881910 | - |
| Language | en | - |
| Extent | 14 pages | en |
| Title | Feature selection algorithms in classification problems: an experimental evaluation | en |
| Creator | Zopounidis Konstantinos | en |
| Creator | Ζοπουνιδης Κωνσταντινος | el |
| Creator | Michael Doumpos | en |
| Creator | Δουμπος Μιχαλης | el |
| Creator | Salappa A. | en |
| Publisher | Taylor & Francis | en |
| Content Summary | 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. | en |
| Type of Item | Peer-Reviewed Journal Publication | en |
| Type of Item | Δημοσίευση σε Περιοδικό με Κριτές | el |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2015-11-17 | - |
| Date of Publication | 2007 | - |
| Subject | Feature selection | en |
| Subject | Knowledge discovery | en |
| Subject | Pattern recognition | en |
| Subject | Machine learning | en |
| 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 | en |
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