RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats
In conventional morphometrics, researchers often collect and analyze data using large numbers of morphometric features to study the shape variation among biological organisms. Feature selection is a fundamental tool in machine learning which is used to remove irrelevant and redundant features. Recur...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Kebangsaan Malaysia
2023
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| Online Access: | http://journalarticle.ukm.my/22553/ http://journalarticle.ukm.my/22553/1/STT%201.pdf |
| _version_ | 1848815627657019392 |
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| author | Aneesha Balachandran Pillay, Dharini Pathmanathan, Arpah Abu, Hasmahzaiti Omar, |
| author_facet | Aneesha Balachandran Pillay, Dharini Pathmanathan, Arpah Abu, Hasmahzaiti Omar, |
| author_sort | Aneesha Balachandran Pillay, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | In conventional morphometrics, researchers often collect and analyze data using large numbers of morphometric features to study the shape variation among biological organisms. Feature selection is a fundamental tool in machine learning which is used to remove irrelevant and redundant features. Recursive feature elimination (RFE) is a popular feature selection technique that reduces data dimensionality and helps in selecting the subset of attributes based on predictor importance ranking. In this study, we perform RFE on the craniodental measurements of the Rattus rattus data to select the best feature subset for both males and females. We also performed a comparative study based on three machine learning algorithms such as Naïve Bayes, Random Forest, and Artificial Neural Network by using all features and the RFE-selected features to classify the R. rattus sample based on the age groups. Artificial Neural Network has shown to provide the best accuracy among these three predictive classification models. |
| first_indexed | 2025-11-15T00:52:59Z |
| format | Article |
| id | oai:generic.eprints.org:22553 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:52:59Z |
| publishDate | 2023 |
| publisher | Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:225532023-11-23T03:11:18Z http://journalarticle.ukm.my/22553/ RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats Aneesha Balachandran Pillay, Dharini Pathmanathan, Arpah Abu, Hasmahzaiti Omar, In conventional morphometrics, researchers often collect and analyze data using large numbers of morphometric features to study the shape variation among biological organisms. Feature selection is a fundamental tool in machine learning which is used to remove irrelevant and redundant features. Recursive feature elimination (RFE) is a popular feature selection technique that reduces data dimensionality and helps in selecting the subset of attributes based on predictor importance ranking. In this study, we perform RFE on the craniodental measurements of the Rattus rattus data to select the best feature subset for both males and females. We also performed a comparative study based on three machine learning algorithms such as Naïve Bayes, Random Forest, and Artificial Neural Network by using all features and the RFE-selected features to classify the R. rattus sample based on the age groups. Artificial Neural Network has shown to provide the best accuracy among these three predictive classification models. Universiti Kebangsaan Malaysia 2023 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/22553/1/STT%201.pdf Aneesha Balachandran Pillay, and Dharini Pathmanathan, and Arpah Abu, and Hasmahzaiti Omar, (2023) RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats. Sains Malaysiana, 52 (7). pp. 1901-1914. ISSN 0126-6039 https://www.ukm.my/jsm/ |
| spellingShingle | Aneesha Balachandran Pillay, Dharini Pathmanathan, Arpah Abu, Hasmahzaiti Omar, RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats |
| title | RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats |
| title_full | RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats |
| title_fullStr | RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats |
| title_full_unstemmed | RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats |
| title_short | RFE-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats |
| title_sort | rfe-based feature selection to improve classification accuracy for morphometric analysis of craniodental characters of house rats |
| url | http://journalarticle.ukm.my/22553/ http://journalarticle.ukm.my/22553/ http://journalarticle.ukm.my/22553/1/STT%201.pdf |