Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers
In this study, a three-phase hybrid approach is proposed for the selection and classification of high dimensional microarray data. The method uses Pearson's Correlation Coefficient (PCC) in combination with Binary Particle Swarm Optimization (BPSO) or Genetic Algorithm (GA) along with various c...
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Online Access: | http://eprints.utm.my/id/eprint/79937/1/RohayantiHassan2018_GeneSelectionandClassificationinMicroarray.880 http://eprints.utm.my/id/eprint/79937/ http://dx.doi.org/10.3844/jcssp.2018.868.880 |
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my.utm.799372019-01-28T07:02:13Z http://eprints.utm.my/id/eprint/79937/ Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers Hameed, S. S. Muhammad, F. F. Hassan, R. Saeed, F. QA76 Computer software In this study, a three-phase hybrid approach is proposed for the selection and classification of high dimensional microarray data. The method uses Pearson's Correlation Coefficient (PCC) in combination with Binary Particle Swarm Optimization (BPSO) or Genetic Algorithm (GA) along with various classifiers, thereby forming a PCC-BPSO/GA-multi classifiers approach. As such, five various classifiers are employed in the final stage of the classification. It was noticed that the PCC filter showed a remarkable improvement in the classification accuracy when it was combined with BPSO or GA. This positive impact was seen to be varied for different datasets based on the final applied classifier. The performance of various combination of the hybrid technique was compared in terms of accuracy and number of selected genes. In addition to the fact that BPSO is working faster than GA, it was noticed that BPSO has better performance than GA when it is combined with PCC feature selection. Science Publications 2018 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79937/1/RohayantiHassan2018_GeneSelectionandClassificationinMicroarray.880 Hameed, S. S. and Muhammad, F. F. and Hassan, R. and Saeed, F. (2018) Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers. Journal of Computer Science, 14 (6). pp. 868-880. ISSN 1549-3636 http://dx.doi.org/10.3844/jcssp.2018.868.880 DOI:10.3844/jcssp.2018.868.880 |
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QA76 Computer software Hameed, S. S. Muhammad, F. F. Hassan, R. Saeed, F. Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers |
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In this study, a three-phase hybrid approach is proposed for the selection and classification of high dimensional microarray data. The method uses Pearson's Correlation Coefficient (PCC) in combination with Binary Particle Swarm Optimization (BPSO) or Genetic Algorithm (GA) along with various classifiers, thereby forming a PCC-BPSO/GA-multi classifiers approach. As such, five various classifiers are employed in the final stage of the classification. It was noticed that the PCC filter showed a remarkable improvement in the classification accuracy when it was combined with BPSO or GA. This positive impact was seen to be varied for different datasets based on the final applied classifier. The performance of various combination of the hybrid technique was compared in terms of accuracy and number of selected genes. In addition to the fact that BPSO is working faster than GA, it was noticed that BPSO has better performance than GA when it is combined with PCC feature selection. |
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Article |
author |
Hameed, S. S. Muhammad, F. F. Hassan, R. Saeed, F. |
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Hameed, S. S. Muhammad, F. F. Hassan, R. Saeed, F. |
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Hameed, S. S. |
title |
Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers |
title_short |
Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers |
title_full |
Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers |
title_fullStr |
Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers |
title_full_unstemmed |
Gene selection and classification in microarray datasets using a hybrid approach of PCC-BPSO/GA with multi classifiers |
title_sort |
gene selection and classification in microarray datasets using a hybrid approach of pcc-bpso/ga with multi classifiers |
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Science Publications |
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2018 |
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http://eprints.utm.my/id/eprint/79937/1/RohayantiHassan2018_GeneSelectionandClassificationinMicroarray.880 http://eprints.utm.my/id/eprint/79937/ http://dx.doi.org/10.3844/jcssp.2018.868.880 |
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