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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Main Authors: Draman @ Muda, Azah Kamilah, Choo, Yun Huoy, Pratama, Satrya Fajri
Format: Article
Language:English
Published: Elsevier 2011
Subjects:
Online Access:http://eprints.utem.edu.my/id/eprint/250/
http://eprints.utem.edu.my/id/eprint/250/1/GCSE_2011.pdf
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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 Institutional Repository
collection Online Access
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.
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institution Universiti Teknikal Malaysia Melaka
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publishDate 2011
publisher Elsevier
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spelling utem-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/
http://eprints.utem.edu.my/id/eprint/250/1/GCSE_2011.pdf