Comparison of multi-objective algorithms applied to feature selection
Studies in Fuzziness and Soft Computing, cilt.285, ss.359-375, 2013 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 285
- Basım Tarihi: 2013
- Doi Numarası: 10.1007/978-3-642-30278-7_28
- Dergi Adı: Studies in Fuzziness and Soft Computing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH
- Sayfa Sayıları: ss.359-375
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Ankara Üniversitesi Adresli: Evet
Özet
The feature selection problem can be formulated as a multiobjective 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 multiobjective methods are applied to four benchmark datasets used in the literature and the obtained results are compared and discussed. © Springer-Verlag 2013.