Journal article
A genetic Algorithm-Based feature selection
International Journal of Electronics Communications and Computer Engineering, Vol.5(4), pp.899-905
2014
Abstract
This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy.
Details
- Title
- A genetic Algorithm-Based feature selection
- Authors/Creators
- B. Oluleye (Author/Creator)L. Armstrong (Author/Creator)J. Leng (Author/Creator)D. Diepeveen (Author/Creator)
- Publication Details
- International Journal of Electronics Communications and Computer Engineering, Vol.5(4), pp.899-905
- Publisher
- Timeline Publication Pvt. Ltd
- Identifiers
- 991005542884607891
- Copyright
- © 2014 IJECCE
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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