Predictive Based Hybrid Ranker To Yield Significant Features In Writer Identification

A. Jalil, Intan Ermahani and Shamsuddin, Siti Mariyam and Muda, Azah Kamilah and Azmi, Mohd Sanusi and Ummi Rabaah, Hashim (2018) Predictive Based Hybrid Ranker To Yield Significant Features In Writer Identification. International Journal Of Advances In Soft Computing And Its Applications, 10 (1). pp. 1-23. ISSN 2074-8523

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Abstract

The contribution of writer identification (WI) towards personal identification in biometrics traits is known because it is easily accessible, cheaper, more reliable and acceptable as compared to other methods such as personal identification based DNA, iris and fingerprint. However, the production of high dimensional datasets has resulted into too many irrelevant or redundant features. These unnecessary features increase the size of the search space and decrease the identification performance. The main problem is to identify the most significant features and select the best subset of features that can precisely predict the authors. Therefore, this study proposed the hybridization of GRA Features Ranking and Feature Subset Selection (GRAFeSS) to develop the best subsets of highest ranking features and developed discretization model with the hybrid method (Dis-GRAFeSS) to improve classification accuracy. Experimental results showed that the methods improved the performance accuracy in identifying the authorship of features based ranking invariant discretization by substantially reducing redundant features.

Item Type:	Article
Uncontrolled Keywords:	Features Ranking, Grey Relational Analysis, Predictive, Significant, Writer Identification
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Information and Communication Technology > Department of Software Engineeering
Depositing User:	Mohd. Nazir Taib
Date Deposited:	10 Jan 2019 07:09
Last Modified:	16 Aug 2021 20:53
URI:	http://eprints.utem.edu.my/id/eprint/21652
Statistic Details:	View Download Statistic

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