Document Type

Journal Article

Publisher

IJECCE

Faculty

Faculty of Health, Engineering and Science

School

School of Computer and Security Science/eAgriculture Research Group

RAS ID

18466

Comments

This article was originally published as: Babatunde, O. H., Armstrong, L. , Leng, J. , & Diepeveen, D. (2014). A Genetic Algorithm-Based Feature Selection. International Journal of Electronics Communication and Computer Engineering, 5(4), 899-905. Original article available here

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.

Access Rights

Open access

IJECCE is an academic Online open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author but weightage should be given to the authors and journal. This is in accordance with the BOAI definition of open access.

Share

 
COinS