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Ideal combination feature selection model for classification problem based on bio-inspired approach

Feature selection or attribute reduction is a crucial process to achieve optimal data reduction for classification task. However, most of the feature selection methods that were introduced work individually that sometimes caused less optimal feature being selected, subsequently degrading the consist...

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Main Authors: Basir, Mohammad Aizat, Hussin, Mohamed Saifullah, Yusof, Yuhanis
Format: Book Section
Published: Springer 2020
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://repo.uum.edu.my/26609/
http://doi.org/10.1007/978-981-15-0058-9_56
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spelling my.uum.repo.266092019-11-20T02:31:48Z http://repo.uum.edu.my/26609/ Ideal combination feature selection model for classification problem based on bio-inspired approach Basir, Mohammad Aizat Hussin, Mohamed Saifullah Yusof, Yuhanis QA75 Electronic computers. Computer science Feature selection or attribute reduction is a crucial process to achieve optimal data reduction for classification task. However, most of the feature selection methods that were introduced work individually that sometimes caused less optimal feature being selected, subsequently degrading the consistency of the classification accuracy rate. The aim of this paper is to exploit the capability of bio-inspired search algorithms, together with wrapper and filtered methods in generating optimal set of features. The important step is to idealize the combined feature selection models by finding the best combination of search method and feature selection algorithms. The next step is to define an optimized feature set for classification task. Performance metrics are analyzed based on classification accuracy and the number of selected features. Experiments were conducted on nine (9) benchmark datasets with various sizes, categorized as small, medium and large dataset. Experimental results revealed that the ideal combination is a feature selection model with the implementation of bio-inspired search algorithm that consistently obtains the optimal solution (i.e. less number of features with higher classification accuracy) on the selected dataset. Such a finding indicates that the exploitation of bio-inspired algorithms with ideal combination of wrapper/filtered method can contribute in finding the optimal features to be used in data mining model construction. Springer 2020 Book Section PeerReviewed Basir, Mohammad Aizat and Hussin, Mohamed Saifullah and Yusof, Yuhanis (2020) Ideal combination feature selection model for classification problem based on bio-inspired approach. In: Computational Science and Technology. Springer, Singapore, pp. 585-593. ISBN 978-981-15-0057-2 http://doi.org/10.1007/978-981-15-0058-9_56 doi:10.1007/978-981-15-0058-9_56
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutional Repository
url_provider http://repo.uum.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Basir, Mohammad Aizat
Hussin, Mohamed Saifullah
Yusof, Yuhanis
Ideal combination feature selection model for classification problem based on bio-inspired approach
description Feature selection or attribute reduction is a crucial process to achieve optimal data reduction for classification task. However, most of the feature selection methods that were introduced work individually that sometimes caused less optimal feature being selected, subsequently degrading the consistency of the classification accuracy rate. The aim of this paper is to exploit the capability of bio-inspired search algorithms, together with wrapper and filtered methods in generating optimal set of features. The important step is to idealize the combined feature selection models by finding the best combination of search method and feature selection algorithms. The next step is to define an optimized feature set for classification task. Performance metrics are analyzed based on classification accuracy and the number of selected features. Experiments were conducted on nine (9) benchmark datasets with various sizes, categorized as small, medium and large dataset. Experimental results revealed that the ideal combination is a feature selection model with the implementation of bio-inspired search algorithm that consistently obtains the optimal solution (i.e. less number of features with higher classification accuracy) on the selected dataset. Such a finding indicates that the exploitation of bio-inspired algorithms with ideal combination of wrapper/filtered method can contribute in finding the optimal features to be used in data mining model construction.
format Book Section
author Basir, Mohammad Aizat
Hussin, Mohamed Saifullah
Yusof, Yuhanis
author_facet Basir, Mohammad Aizat
Hussin, Mohamed Saifullah
Yusof, Yuhanis
author_sort Basir, Mohammad Aizat
title Ideal combination feature selection model for classification problem based on bio-inspired approach
title_short Ideal combination feature selection model for classification problem based on bio-inspired approach
title_full Ideal combination feature selection model for classification problem based on bio-inspired approach
title_fullStr Ideal combination feature selection model for classification problem based on bio-inspired approach
title_full_unstemmed Ideal combination feature selection model for classification problem based on bio-inspired approach
title_sort ideal combination feature selection model for classification problem based on bio-inspired approach
publisher Springer
publishDate 2020
url http://repo.uum.edu.my/26609/
http://doi.org/10.1007/978-981-15-0058-9_56
_version_ 1651870667651416064
score 13.235362

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