A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information Technique
Feature selection is one of the most important issues in improving the data classification process. It greatly influences the accuracy of the classification. There are many evolutionary algorithms used for this purpose, such as the Particle Swarm Optimization (PSO) in discrete space through the Bina...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
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Mosul University
2019-12-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.mosuljournals.com/article_163520_78fc9eceac26796879717e46215b4a51.pdf |
| _version_ | 1818107598325415936 |
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| author | Mustafa Abed Alhafedh Omar Qasim |
| author_facet | Mustafa Abed Alhafedh Omar Qasim |
| author_sort | Mustafa Abed Alhafedh |
| collection | DOAJ |
| description | Feature selection is one of the most important issues in improving the data classification process. It greatly influences the accuracy of the classification. There are many evolutionary algorithms used for this purpose, such as the Particle Swarm Optimization (PSO) in discrete space through the Binary PSO concept. The BPSO optimization algorithm derives its mechanism from the default PSO algorithm but in discrete space. In this research, a hybrid approach was proposed between the BPSO algorithm and Mutual Information (MI) to obtain subsets of features through two basic phases: the first is to use the BPSO algorithm to determine the features affecting the data classification process by relying on an objective function. In the second phase, the MI method is used to reduce the number of features identified by the BPSO method. The results of the proposed algorithm have demonstrated efficiency and effectiveness by obtaining higher classification accuracy and using fewer features than default methods.<br /> |
| first_indexed | 2024-12-11T02:02:00Z |
| format | Article |
| id | doaj.art-5ef92145478d441c8eefca7e627fd1c0 |
| institution | Directory Open Access Journal |
| issn | 1815-4816 2311-7990 |
| language | Arabic |
| last_indexed | 2024-12-11T02:02:00Z |
| publishDate | 2019-12-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj.art-5ef92145478d441c8eefca7e627fd1c02022-12-22T01:24:28ZaraMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902019-12-01132496010.33899/csmj.2020.163520163520A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information TechniqueMustafa Abed Alhafedh0Omar Qasim1College of Computer Science and Mathematics University of Mosul, Mosul, IraqCollege of Computer Science and Mathematics University of Mosul, Mosul, IraqFeature selection is one of the most important issues in improving the data classification process. It greatly influences the accuracy of the classification. There are many evolutionary algorithms used for this purpose, such as the Particle Swarm Optimization (PSO) in discrete space through the Binary PSO concept. The BPSO optimization algorithm derives its mechanism from the default PSO algorithm but in discrete space. In this research, a hybrid approach was proposed between the BPSO algorithm and Mutual Information (MI) to obtain subsets of features through two basic phases: the first is to use the BPSO algorithm to determine the features affecting the data classification process by relying on an objective function. In the second phase, the MI method is used to reduce the number of features identified by the BPSO method. The results of the proposed algorithm have demonstrated efficiency and effectiveness by obtaining higher classification accuracy and using fewer features than default methods.<br />https://csmj.mosuljournals.com/article_163520_78fc9eceac26796879717e46215b4a51.pdffeature selectionparticle swarm optimizationmutual information techniqueclassification |
| spellingShingle | Mustafa Abed Alhafedh Omar Qasim A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information Technique Al-Rafidain Journal of Computer Sciences and Mathematics feature selection particle swarm optimization mutual information technique classification |
| title | A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information Technique |
| title_full | A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information Technique |
| title_fullStr | A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information Technique |
| title_full_unstemmed | A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information Technique |
| title_short | A Proposed Method for Feature Selection using a Binary Particle Swarm Optimization Algorithm and Mutual Information Technique |
| title_sort | proposed method for feature selection using a binary particle swarm optimization algorithm and mutual information technique |
| topic | feature selection particle swarm optimization mutual information technique classification |
| url | https://csmj.mosuljournals.com/article_163520_78fc9eceac26796879717e46215b4a51.pdf |
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