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

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

Liau Heng Fui, Dino Isa
Copyright: © 2011 |Volume: 2 |Issue: 1 |Pages: 16
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781613505533|DOI: 10.4018/jaec.2011010102
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MLA

Fui, Liau Heng, and Dino Isa. "Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve." IJAEC vol.2, no.1 2011: pp.18-33. https://doi.org/10.4018/jaec.2011010102

APA

Fui, L. H. & Isa, D. (2011). Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve. International Journal of Applied Evolutionary Computation (IJAEC), 2(1), 18-33. https://doi.org/10.4018/jaec.2011010102

Chicago

Fui, Liau Heng, and Dino Isa. "Feature Selection Based on Minimizing the Area Under the Detection Error Tradeoff Curve," International Journal of Applied Evolutionary Computation (IJAEC) 2, no.1: 18-33. https://doi.org/10.4018/jaec.2011010102

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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).

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