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SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem

In supervised learning, imbalanced class dataset is a state where the class distribution is not uniform among the classes. Most standard classifiers fail to properly identify pattern that belongs to minority class because most of those classifiers are built to minimize the error rate. As a result, a...

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Main Authors: Mohd Pozi, Muhammad Syafiq, Azhar, Nur Athirah, Abdul Raziff, Abdul Rafiez, Ajrina, Lina Hazmi
Format: Article
Language:English
English
English
Published: Springer Nature 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/115974/1/115974_SVGPM%20evolving%20SVM%20decision.pdf
http://irep.iium.edu.my/115974/7/115974_SVGPM%20evolving%20SVM%20decision_SCOPUS.pdf
http://irep.iium.edu.my/115974/8/115974_SVGPM%20evolving%20SVM%20decision_WOS.pdf
http://irep.iium.edu.my/115974/
https://link.springer.com/article/10.1007/s13748-021-00260-4
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spelling my.iium.irep.1159742024-11-22T08:34:41Z http://irep.iium.edu.my/115974/ SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem Mohd Pozi, Muhammad Syafiq Azhar, Nur Athirah Abdul Raziff, Abdul Rafiez Ajrina, Lina Hazmi QA75 Electronic computers. Computer science In supervised learning, imbalanced class dataset is a state where the class distribution is not uniform among the classes. Most standard classifiers fail to properly identify pattern that belongs to minority class because most of those classifiers are built to minimize the error rate. As a result, a biased classification model is highly anticipated, as higher accuracy metrics can solely be represented by the majority class. In order to tackle this problem, several methods have been proposed, mainly to reduce the classifier’s bias, such as performing resampling on the dataset, modification on a classifier optimization problem, or introducing a new optimization task on top of the classifier. Our proposal is based on a new optimization task on top of a classifier, combined as a part of the learning process. Specifically, a hybrid classifier based on genetic programming and support vector machines is proposed. Our classifier has shown to be competitive enough against several variations of support vector machines in solving imbalanced classification problem from the experimentation carried out. Springer Nature 2022-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/115974/1/115974_SVGPM%20evolving%20SVM%20decision.pdf application/pdf en http://irep.iium.edu.my/115974/7/115974_SVGPM%20evolving%20SVM%20decision_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/115974/8/115974_SVGPM%20evolving%20SVM%20decision_WOS.pdf Mohd Pozi, Muhammad Syafiq and Azhar, Nur Athirah and Abdul Raziff, Abdul Rafiez and Ajrina, Lina Hazmi (2022) SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem. Progress in Artificial Intelligence, 11 (1). pp. 65-77. ISSN 2192-6352 E-ISSN 2192-6360 https://link.springer.com/article/10.1007/s13748-021-00260-4 10.1007/s13748-021-00260-4
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mohd Pozi, Muhammad Syafiq
Azhar, Nur Athirah
Abdul Raziff, Abdul Rafiez
Ajrina, Lina Hazmi
SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem
description In supervised learning, imbalanced class dataset is a state where the class distribution is not uniform among the classes. Most standard classifiers fail to properly identify pattern that belongs to minority class because most of those classifiers are built to minimize the error rate. As a result, a biased classification model is highly anticipated, as higher accuracy metrics can solely be represented by the majority class. In order to tackle this problem, several methods have been proposed, mainly to reduce the classifier’s bias, such as performing resampling on the dataset, modification on a classifier optimization problem, or introducing a new optimization task on top of the classifier. Our proposal is based on a new optimization task on top of a classifier, combined as a part of the learning process. Specifically, a hybrid classifier based on genetic programming and support vector machines is proposed. Our classifier has shown to be competitive enough against several variations of support vector machines in solving imbalanced classification problem from the experimentation carried out.
format Article
author Mohd Pozi, Muhammad Syafiq
Azhar, Nur Athirah
Abdul Raziff, Abdul Rafiez
Ajrina, Lina Hazmi
author_facet Mohd Pozi, Muhammad Syafiq
Azhar, Nur Athirah
Abdul Raziff, Abdul Rafiez
Ajrina, Lina Hazmi
author_sort Mohd Pozi, Muhammad Syafiq
title SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem
title_short SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem
title_full SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem
title_fullStr SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem
title_full_unstemmed SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem
title_sort svgpm: evolving svm decision function by using genetic programming to solve imbalanced classification problem
publisher Springer Nature
publishDate 2022
url http://irep.iium.edu.my/115974/1/115974_SVGPM%20evolving%20SVM%20decision.pdf
http://irep.iium.edu.my/115974/7/115974_SVGPM%20evolving%20SVM%20decision_SCOPUS.pdf
http://irep.iium.edu.my/115974/8/115974_SVGPM%20evolving%20SVM%20decision_WOS.pdf
http://irep.iium.edu.my/115974/
https://link.springer.com/article/10.1007/s13748-021-00260-4
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score 13.223943

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