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...

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Main Authors: Pratama, S. F., Muda, A. K., Yun-Huoy, C.
Format: Conference or Workshop Item
Language:English
Published: 2010
Subjects:
Online Access:http://eprints.utem.edu.my/id/eprint/39/
http://eprints.utem.edu.my/id/eprint/39/1/rp053_Vol.1-I100.pdf
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author Pratama, S. F.
Muda, A. K.
Yun-Huoy, C.
author_facet Pratama, S. F.
Muda, A. K.
Yun-Huoy, C.
author_sort Pratama, S. F.
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.
first_indexed 2025-11-15T19:45:27Z
format Conference or Workshop Item
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institution Universiti Teknikal Malaysia Melaka
institution_category Local University
language English
last_indexed 2025-11-15T19:45:27Z
publishDate 2010
recordtype eprints
repository_type Digital Repository
spelling utem-392015-05-28T02:16:33Z http://eprints.utem.edu.my/id/eprint/39/ Feature Selection Methods for Writer Identification: A Comparative Study Pratama, S. F. Muda, A. K. Yun-Huoy, C. TA Engineering (General). Civil engineering (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. 2010 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/39/1/rp053_Vol.1-I100.pdf Pratama, S. F. and Muda, A. K. and Yun-Huoy, C. (2010) Feature Selection Methods for Writer Identification: A Comparative Study. In: IEEE International Conference on Computer and Computational Intelligence (ICCCI 2010), 25 - 26 December, 2010, Nanning, China.
spellingShingle TA Engineering (General). Civil engineering (General)
Pratama, S. F.
Muda, A. K.
Yun-Huoy, C.
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 TA Engineering (General). Civil engineering (General)
url http://eprints.utem.edu.my/id/eprint/39/
http://eprints.utem.edu.my/id/eprint/39/1/rp053_Vol.1-I100.pdf