Phylogenetic tree classification system using machine learning algorithm
A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. This study adopted supervised machine learning algorithm which is the Support Vector Machine (SVM) for classification. Image data were collected from online databases PUBME...
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| Format: | Final Year Project Report / IMRAD |
| Language: | English English |
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Universiti Malaysia Sarawak, (UNIMAS)
2015
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| Online Access: | http://ir.unimas.my/id/eprint/10339/ http://ir.unimas.my/id/eprint/10339/1/Phylogenetic%20Tree%20Classification%20System%20Using%20Machine%20Learning%20Algorithm%20%2824pgs%29.pdf http://ir.unimas.my/id/eprint/10339/8/Tan%20Jia%20Kae.pdf |
| _version_ | 1848836758886678528 |
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| author | Tan, Jia Kae |
| author_facet | Tan, Jia Kae |
| author_sort | Tan, Jia Kae |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. This study adopted supervised machine learning algorithm which is the Support Vector Machine (SVM) for classification. Image data were collected from online databases PUBMED, ScienceDirect and Bioinfonnatic journals. Perfonnance comparisons of three types of features to characterize the phylogenetic tree images are presented in this project. The aim is to detennine the suitable features for the phylogenetic tree image classification systeIlJ. The leave-out one cross-validation was used to calculate the accuracy of each feature. In addition to that, 10-fold cross-validation is also conducted in the evaluation. Our results show that the suitable combination features for the phylogenetic tree image classification system are SIFT, SURF and GIST. The accuracy obtained from these combinations of the three features can achieve just over 82%. On the other hands, the results show the average accuracy obtained from the 10-fold cross-validation is 81.50%. Our evaluation results demonstrate the utility of using SIFT, SURF and GIST features for building phylogenetic tree image classification system. |
| first_indexed | 2025-11-15T06:28:52Z |
| format | Final Year Project Report / IMRAD |
| id | unimas-10339 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T06:28:52Z |
| publishDate | 2015 |
| publisher | Universiti Malaysia Sarawak, (UNIMAS) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-103392024-04-01T02:18:33Z http://ir.unimas.my/id/eprint/10339/ Phylogenetic tree classification system using machine learning algorithm Tan, Jia Kae L Education (General) T Technology (General) A study is conducted to develop an automated phylogenetic tree image classification system by using machine learning algorithm. This study adopted supervised machine learning algorithm which is the Support Vector Machine (SVM) for classification. Image data were collected from online databases PUBMED, ScienceDirect and Bioinfonnatic journals. Perfonnance comparisons of three types of features to characterize the phylogenetic tree images are presented in this project. The aim is to detennine the suitable features for the phylogenetic tree image classification systeIlJ. The leave-out one cross-validation was used to calculate the accuracy of each feature. In addition to that, 10-fold cross-validation is also conducted in the evaluation. Our results show that the suitable combination features for the phylogenetic tree image classification system are SIFT, SURF and GIST. The accuracy obtained from these combinations of the three features can achieve just over 82%. On the other hands, the results show the average accuracy obtained from the 10-fold cross-validation is 81.50%. Our evaluation results demonstrate the utility of using SIFT, SURF and GIST features for building phylogenetic tree image classification system. Universiti Malaysia Sarawak, (UNIMAS) 2015 Final Year Project Report / IMRAD NonPeerReviewed text en http://ir.unimas.my/id/eprint/10339/1/Phylogenetic%20Tree%20Classification%20System%20Using%20Machine%20Learning%20Algorithm%20%2824pgs%29.pdf text en http://ir.unimas.my/id/eprint/10339/8/Tan%20Jia%20Kae.pdf Tan, Jia Kae (2015) Phylogenetic tree classification system using machine learning algorithm. [Final Year Project Report / IMRAD] |
| spellingShingle | L Education (General) T Technology (General) Tan, Jia Kae Phylogenetic tree classification system using machine learning algorithm |
| title | Phylogenetic tree classification system using machine learning algorithm |
| title_full | Phylogenetic tree classification system using machine learning algorithm |
| title_fullStr | Phylogenetic tree classification system using machine learning algorithm |
| title_full_unstemmed | Phylogenetic tree classification system using machine learning algorithm |
| title_short | Phylogenetic tree classification system using machine learning algorithm |
| title_sort | phylogenetic tree classification system using machine learning algorithm |
| topic | L Education (General) T Technology (General) |
| url | http://ir.unimas.my/id/eprint/10339/ http://ir.unimas.my/id/eprint/10339/1/Phylogenetic%20Tree%20Classification%20System%20Using%20Machine%20Learning%20Algorithm%20%2824pgs%29.pdf http://ir.unimas.my/id/eprint/10339/8/Tan%20Jia%20Kae.pdf |