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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| Format: | Article |
| Language: | English |
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Taylor & Francis
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/43520/ http://psasir.upm.edu.my/id/eprint/43520/1/A%20new%20classification%20model%20for%20a%20class%20imbalanced%20data%20set%20using%20genetic%20programming.pdf |
| _version_ | 1848850250387685376 |
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| author | Mohd Pozi, Muhammad Syafiq Sulaiman, Md Nasir Mustapha, Norwati Perumal, Thinagaran |
| author_facet | Mohd Pozi, Muhammad Syafiq Sulaiman, Md Nasir Mustapha, Norwati Perumal, Thinagaran |
| author_sort | Mohd Pozi, Muhammad Syafiq |
| building | UPM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T10:03:18Z |
| format | Article |
| id | upm-43520 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:03:18Z |
| publishDate | 2015 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-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 |
| 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 |
| 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_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_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_sort | new classification model for a class imbalanced data set using genetic programming and support vector machines: case study for wilt disease classification |
| url | http://psasir.upm.edu.my/id/eprint/43520/ http://psasir.upm.edu.my/id/eprint/43520/ http://psasir.upm.edu.my/id/eprint/43520/1/A%20new%20classification%20model%20for%20a%20class%20imbalanced%20data%20set%20using%20genetic%20programming.pdf |