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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

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, mod...

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Main Authors: Mohd Pozi, Muhammad Syafiq, Sulaiman, Md Nasir, Mustapha, Norwati, Perumal, Thinagaran
Format: Article
Language:English
Published: Taylor & Francis 2015
Online Access:http://psasir.upm.edu.my/id/eprint/43520/1/A%20new%20classification%20model%20for%20a%20class%20imbalanced%20data%20set%20using%20genetic%20programming.pdf
http://psasir.upm.edu.my/id/eprint/43520/
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spelling my.upm.eprints.435202018-04-09T04:26:51Z http://psasir.upm.edu.my/id/eprint/43520/ A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification Mohd Pozi, Muhammad Syafiq Sulaiman, Md Nasir Mustapha, Norwati Perumal, Thinagaran 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. Taylor & Francis 2015-07 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/43520/1/A%20new%20classification%20model%20for%20a%20class%20imbalanced%20data%20set%20using%20genetic%20programming.pdf 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 10.1080/2150704X.2015.1062159
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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.
format Article
author Mohd Pozi, Muhammad Syafiq
Sulaiman, Md Nasir
Mustapha, Norwati
Perumal, Thinagaran
spellingShingle Mohd Pozi, Muhammad Syafiq
Sulaiman, Md Nasir
Mustapha, Norwati
Perumal, Thinagaran
A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
author_facet Mohd Pozi, Muhammad Syafiq
Sulaiman, Md Nasir
Mustapha, Norwati
Perumal, Thinagaran
author_sort Mohd Pozi, Muhammad Syafiq
title A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
title_short A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
title_full A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
title_fullStr A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
title_full_unstemmed A new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
title_sort new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification
publisher Taylor & Francis
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/43520/1/A%20new%20classification%20model%20for%20a%20class%20imbalanced%20data%20set%20using%20genetic%20programming.pdf
http://psasir.upm.edu.my/id/eprint/43520/
_version_ 1643833590059892736
score 13.211869

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