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

Pozi, Muhammad Syafiq Mohd and Azhar, Nur Athirah and Raziff, Abdul Rafiez Abdul 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.

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Abstract

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.

Item Type:	Article (Journal)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Dr Abdul Rafiez Abdul Raziff
Date Deposited:	21 Nov 2024 11:57
Last Modified:	21 Nov 2024 11:57
URI:	http://irep.iium.edu.my/id/eprint/115974

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