Predictive based hybrid ranker to yield significant features in writer identification
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...
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International Center for Scientific Research and Studies
2018
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Online Access: | http://eprints.utm.my/id/eprint/85804/1/SitiMariyamShamsuddin2018_PredictiveBasedHybridRanker.pdf http://eprints.utm.my/id/eprint/85804/ http://home.ijasca.com/data/documents/1_PP1_23_Predictive-based-Hybrid-Ranker-to-Yield.pdf |
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my.utm.858042020-07-28T02:45:33Z http://eprints.utm.my/id/eprint/85804/ Predictive based hybrid ranker to yield significant features in writer identification A. Jalil, Intan Ermahani Shamsuddin, Siti Mariyam Muda, Azah Kamilah Azmi, Mohd. Sanusi Raba'ah, Hashim Ummi QA75 Electronic computers. Computer science 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. International Center for Scientific Research and Studies 2018-03 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/85804/1/SitiMariyamShamsuddin2018_PredictiveBasedHybridRanker.pdf 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 http://home.ijasca.com/data/documents/1_PP1_23_Predictive-based-Hybrid-Ranker-to-Yield.pdf |
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QA75 Electronic computers. Computer science A. Jalil, Intan Ermahani Shamsuddin, Siti Mariyam Muda, Azah Kamilah Azmi, Mohd. Sanusi Raba'ah, Hashim Ummi Predictive based hybrid ranker to yield significant features in writer identification |
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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. |
format |
Article |
author |
A. Jalil, Intan Ermahani Shamsuddin, Siti Mariyam Muda, Azah Kamilah Azmi, Mohd. Sanusi Raba'ah, Hashim Ummi |
author_facet |
A. Jalil, Intan Ermahani Shamsuddin, Siti Mariyam Muda, Azah Kamilah Azmi, Mohd. Sanusi Raba'ah, Hashim Ummi |
author_sort |
A. Jalil, Intan Ermahani |
title |
Predictive based hybrid ranker to yield significant features in writer identification |
title_short |
Predictive based hybrid ranker to yield significant features in writer identification |
title_full |
Predictive based hybrid ranker to yield significant features in writer identification |
title_fullStr |
Predictive based hybrid ranker to yield significant features in writer identification |
title_full_unstemmed |
Predictive based hybrid ranker to yield significant features in writer identification |
title_sort |
predictive based hybrid ranker to yield significant features in writer identification |
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International Center for Scientific Research and Studies |
publishDate |
2018 |
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http://eprints.utm.my/id/eprint/85804/1/SitiMariyamShamsuddin2018_PredictiveBasedHybridRanker.pdf http://eprints.utm.my/id/eprint/85804/ http://home.ijasca.com/data/documents/1_PP1_23_Predictive-based-Hybrid-Ranker-to-Yield.pdf |
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