Feature selection methods for writer identification: A comparative study
Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of feature selection in this domain. Various filter and wrapper fe...
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| Format: | Article |
| Language: | en |
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Elsevier
2011
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| Online Access: | http://eprints.utem.edu.my/id/eprint/250/1/GCSE_2011.pdf http://eprints.utem.edu.my/id/eprint/250/ |
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| _version_ | 1832715952618209280 |
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| author | Draman @ Muda, Azah Kamilah Choo, Yun Huoy Pratama, Satrya Fajri |
| author_facet | Draman @ Muda, Azah Kamilah Choo, Yun Huoy Pratama, Satrya Fajri |
| author_sort | Draman @ Muda, Azah Kamilah |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of feature selection in this domain. Various filter and wrapper feature selection methods are selected and their performances are analyzed using image dataset from IAM Handwriting Database. It is also analyzed the number of features selected and the accuracy of these methods, and then evaluated and compared each method on the basis of these measurements. The evaluation identifies the most interesting method to be further explored and adapted in the future works to fully compatible with Writer Identification domain. |
| format | Article |
| id | my.utem.eprints-250 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2011 |
| publisher | Elsevier |
| record_format | eprints |
| spelling | my.utem.eprints-2502023-08-16T16:09:34Z http://eprints.utem.edu.my/id/eprint/250/ Feature selection methods for writer identification: A comparative study Draman @ Muda, Azah Kamilah Choo, Yun Huoy Pratama, Satrya Fajri T Technology (General) Feature selection is an important area in the machine learning, specifically in pattern recognition. However, it has not received so many focuses in Writer Identification domain. Therefore, this paper is meant for exploring the usage of feature selection in this domain. Various filter and wrapper feature selection methods are selected and their performances are analyzed using image dataset from IAM Handwriting Database. It is also analyzed the number of features selected and the accuracy of these methods, and then evaluated and compared each method on the basis of these measurements. The evaluation identifies the most interesting method to be further explored and adapted in the future works to fully compatible with Writer Identification domain. Elsevier 2011 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/250/1/GCSE_2011.pdf Draman @ Muda, Azah Kamilah and Choo, Yun Huoy and Pratama, Satrya Fajri (2011) Feature selection methods for writer identification: A comparative study. International Journal on Procedia Engineering 2011. pp. 1-10. |
| spellingShingle | T Technology (General) Draman @ Muda, Azah Kamilah Choo, Yun Huoy Pratama, Satrya Fajri Feature selection methods for writer identification: A comparative study |
| title | Feature selection methods for writer identification: A comparative study |
| title_full | Feature selection methods for writer identification: A comparative study |
| title_fullStr | Feature selection methods for writer identification: A comparative study |
| title_full_unstemmed | Feature selection methods for writer identification: A comparative study |
| title_short | Feature selection methods for writer identification: A comparative study |
| title_sort | feature selection methods for writer identification: a comparative study |
| topic | T Technology (General) |
| url | http://eprints.utem.edu.my/id/eprint/250/1/GCSE_2011.pdf http://eprints.utem.edu.my/id/eprint/250/ |
| url_provider | http://eprints.utem.edu.my/ |