Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve

Liau Heng Fui and Dino Isa
Additional contact information
Liau Heng Fui: University of Nottingham Malaysia, Malaysia
Dino Isa: University of Nottingham Malaysia, Malaysia

International Journal of Applied Evolutionary Computation (IJAEC), 2011, vol. 2, issue 1, 18-33

Abstract: Feature selection is crucial to select an “optimized” subset of features from the original feature set based on a certain objective function. In general, feature selection removes redundant or irrelevant data while retaining classification accuracy. This paper proposes a feature selection algorithm that aims to minimize the area under the curve of detection error trade-off (DET) curve. Particle swarm optimization (PSO) is employed to search for the optimal feature subset. The proposed method is implemented in face recognition and iris recognition systems. The result shows that the proposed method is able to find an optimal subset of features that sufficiently describes iris and face images by removing unwanted and redundant features and at the same time improving the classification accuracy in terms of total error rate (TER).

Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jaec.2011010102 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jaec00:v:2:y:2011:i:1:p:18-33

Access Statistics for this article

International Journal of Applied Evolutionary Computation (IJAEC) is currently edited by Sukhpal Singh Gill

More articles in International Journal of Applied Evolutionary Computation (IJAEC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().