Feature selection methods for writer identification: a comparative study

Muda, A. K. and Choo, Yun Huoy (2013) Feature selection methods for writer identification: a comparative study. TRANSACTION ON ELECTRICAL AND ELECTRONIC CIRCUITS AND SYSTEMS. pp. 10-16. ISSN 030303001000162013000178

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Abstract

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.

Item Type: Article
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Information and Communication Technology > Department of Software Engineeering
Depositing User: Azah Kamilah Muda
Date Deposited: 25 Mar 2014 08:13
Last Modified: 28 May 2015 04:21
URI: http://eprints.utem.edu.my/id/eprint/11926
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