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A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification


Citation

Mohd Pozi, Muhammad Syafiq and Sulaiman, Md Nasir and Mustapha, Norwati and Perumal, Thinagaran (2015) A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification. Remote Sensing Letters, 6 (7). pp. 568-577. ISSN 2150-704X; ESSN: 2150-7058

Abstract

Class imbalanced data set is a state where each class of the given data set is not evenly distributed. When such case happens, most standard classifiers fail to recognize examples that belong to a minority class. Hence, several methods have been proposed to solve this problem such as resampling, modification on classifier optimization problem or introducing a new optimization task on top of the classifier. This work proposes a new optimization task based on genetic programming, built on top of support vector machine, in order to improve the classification rate for minority class without significant reduction on accuracy metric. The experimentation carried out on wilt disease data set shows the new classifier, support vector based on genetic programming machine, gives a more balanced accuracy between classes compared to various classification techniques in solving the imbalanced classification problem.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1080/2150704X.2015.1062159
Publisher: Taylor & Francis
Keywords: Class imbalance; Genetic programming; Vector machine; Remote sensing
Depositing User: Mohd Hafiz Che Mahasan
Date Deposited: 09 Apr 2018 04:26
Last Modified: 09 Apr 2018 04:26
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1080/2150704X.2015.1062159
URI: http://psasir.upm.edu.my/id/eprint/43520
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