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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| Format: | Article |
| Language: | English |
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Penerbit UMP
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40090/ http://umpir.ump.edu.my/id/eprint/40090/1/Supervised%20Feature%20Selection%20based%20on%20the%20Law%20of%20Total%20Variance.pdf |
| _version_ | 1848825950848942080 |
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| author | Nur Atiqah, Mustapa Azlyna, Senawi Wei, Hua-Liang |
| author_facet | Nur Atiqah, Mustapa Azlyna, Senawi Wei, Hua-Liang |
| author_sort | Nur Atiqah, Mustapa |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:37:04Z |
| format | Article |
| id | ump-40090 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:37:04Z |
| publishDate | 2023 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| spellingShingle | QA Mathematics Nur Atiqah, Mustapa Azlyna, Senawi Wei, Hua-Liang Supervised feature selection based on the law of total variance |
| title | 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_short | Supervised feature selection based on the law of total variance |
| title_sort | supervised feature selection based on the law of total variance |
| topic | QA Mathematics |
| url | http://umpir.ump.edu.my/id/eprint/40090/ http://umpir.ump.edu.my/id/eprint/40090/ http://umpir.ump.edu.my/id/eprint/40090/ http://umpir.ump.edu.my/id/eprint/40090/1/Supervised%20Feature%20Selection%20based%20on%20the%20Law%20of%20Total%20Variance.pdf |