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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 Raba'ah, Hashim Ummi (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:Predictive, Significant
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:Science
ID Code:85804
Deposited By: Widya Wahid
Deposited On:28 Jul 2020 10:45
Last Modified:28 Jul 2020 10:45

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