Comparison of multi-objective algorithms applied to feature selection

Türkşen, Özlem; Vieira, Susana M.; Madeira, JFA; Apaydin, Aysen; Sousa, João M. C.

http://hdl.handle.net/10400.21/6984

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
Comparison_multi_objective.pdf		11.16 MB	Adobe PDF	Download

Send Feedback

Authors

Abstract(s)

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.

URI

http://hdl.handle.net/10400.21/6984

Citation

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.