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Supervised feature selection based on the law of total variance

Feature selection is a fundamental pre-processing step in machine learning that decreases data dimensionality by removing superfluous and irrelevant features. This study proposes a supervised feature selection method based on feature relevance by employing the law of total variance (LTV). Specifical...

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Main Authors: Nur Atiqah, Mustapa, Azlyna, Senawi, Wei, Hua-Liang
Format: Article
Language:English
Published: Penerbit UMP 2023
Subjects:
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/40090/1/Supervised%20Feature%20Selection%20based%20on%20the%20Law%20of%20Total%20Variance.pdf
http://umpir.ump.edu.my/id/eprint/40090/
https://doi.org/10.15282/mekatronika.v5i2.9998
https://doi.org/10.15282/mekatronika.v5i2.9998
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spelling my.ump.umpir.400902024-01-18T07:16:55Z http://umpir.ump.edu.my/id/eprint/40090/ Supervised feature selection based on the law of total variance Nur Atiqah, Mustapa Azlyna, Senawi Wei, Hua-Liang QA Mathematics Feature selection is a fundamental pre-processing step in machine learning that decreases data dimensionality by removing superfluous and irrelevant features. This study proposes a supervised feature selection method based on feature relevance by employing the law of total variance (LTV). Specifically, the LTV is used to quantify the relevance of features by analysing the association between features and class label. Six classifiers were employed to evaluate the performance and reliability of the proposed method pertaining to classification accuracy. The results proved that a feature subset given by the proposed method has the capability to achieve comparable classification accuracy to the full feature set when just half or less than half of the original features are retained. The proposed method was also proven to be versatile as it can achieves adequate classification accuracy with all six classifiers with different learning schemes. In addition, a comparison with a similar type of feature selection method (AmRMR) shows that the proposed method yields a more accurate classification. Penerbit UMP 2023-07 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/40090/1/Supervised%20Feature%20Selection%20based%20on%20the%20Law%20of%20Total%20Variance.pdf Nur Atiqah, Mustapa and Azlyna, Senawi and Wei, Hua-Liang (2023) Supervised feature selection based on the law of total variance. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 5 (2). pp. 100-110. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v5i2.9998 https://doi.org/10.15282/mekatronika.v5i2.9998
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Nur Atiqah, Mustapa
Azlyna, Senawi
Wei, Hua-Liang
Supervised feature selection based on the law of total variance
description Feature selection is a fundamental pre-processing step in machine learning that decreases data dimensionality by removing superfluous and irrelevant features. This study proposes a supervised feature selection method based on feature relevance by employing the law of total variance (LTV). Specifically, the LTV is used to quantify the relevance of features by analysing the association between features and class label. Six classifiers were employed to evaluate the performance and reliability of the proposed method pertaining to classification accuracy. The results proved that a feature subset given by the proposed method has the capability to achieve comparable classification accuracy to the full feature set when just half or less than half of the original features are retained. The proposed method was also proven to be versatile as it can achieves adequate classification accuracy with all six classifiers with different learning schemes. In addition, a comparison with a similar type of feature selection method (AmRMR) shows that the proposed method yields a more accurate classification.
format Article
author Nur Atiqah, Mustapa
Azlyna, Senawi
Wei, Hua-Liang
author_facet Nur Atiqah, Mustapa
Azlyna, Senawi
Wei, Hua-Liang
author_sort Nur Atiqah, Mustapa
title Supervised feature selection based on the law of total variance
title_short Supervised feature selection based on the law of total variance
title_full Supervised feature selection based on the law of total variance
title_fullStr Supervised feature selection based on the law of total variance
title_full_unstemmed Supervised feature selection based on the law of total variance
title_sort supervised feature selection based on the law of total variance
publisher Penerbit UMP
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40090/1/Supervised%20Feature%20Selection%20based%20on%20the%20Law%20of%20Total%20Variance.pdf
http://umpir.ump.edu.my/id/eprint/40090/
https://doi.org/10.15282/mekatronika.v5i2.9998
https://doi.org/10.15282/mekatronika.v5i2.9998
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score 13.244413

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