Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10400.21/6939
Título: | Comparison of multi-objective algorithms applied to feature selection |
Autor: | Türkşen, Özlem Vieira, Susana M. Madeira, JFA Apaydin, Aysen |
Data: | 2013 |
Editora: | Springer Verlag |
Citação: | TÜRKSEN, Özlem; [et al] – Comparison of multi-objective algorithms applied to feature selection. Studies in Fuzziness and Soft Computing. ISSN 1434-9922. Vol. 285, (2013), pp. 359-375. |
Resumo: | The feature selection problem can be formulated as a multi-objective optimization (MOO) problem, as it involves the minimization of the feature subset cardinality and the misclassification error. In this chapter, a comparison of MOO algorithms applied to feature selection is presented. The used MOO methods are: Nondominated Sorting Genetic Algorithm II (NSGA-II), Archived Multi Objective Simulated Annealing (AMOSA), and Direct Multi Search (DMS). To test the feature subset solutions, Takagi- Sugeno fuzzy models are used as classifiers. To solve the feature selection problem, AMOSA was adapted to deal with discrete optimization. The multi-objective methods are applied to four benchmark datasets used in the literature and the obtained results are compared and discussed. |
Peer review: | yes |
URI: | http://hdl.handle.net/10400.21/6939 |
DOI: | 10.1007/978-3-642-30278-7_28 |
ISSN: | 1434-9922 |
Aparece nas colecções: | ISEL - Matemática - Artigos |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Comparison_multi_objective.pdf | 11,43 MB | Adobe PDF | Ver/Abrir |
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