Hybrid flower pollination algorithm and support vector machine for breast cancer classification
Microarray technology is a system that enable experts to examine gene profile at molecular level for early disease detection. Machine learning algorithms such as classification are used in detection of dieses from data generated by microarray. It increases the potentials of classification and diagno...
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Journal of Technology Management and Business
2018
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my.utm.823462019-11-26T07:39:13Z http://eprints.utm.my/id/eprint/82346/ Hybrid flower pollination algorithm and support vector machine for breast cancer classification Mohamed Radzi, Nor Haizan Salleh @ Sallehuddin, Roselina Mustaffa, Noorfa Haszlinna Dankolo, Muhammad Nasiru QA75 Electronic computers. Computer science Microarray technology is a system that enable experts to examine gene profile at molecular level for early disease detection. Machine learning algorithms such as classification are used in detection of dieses from data generated by microarray. It increases the potentials of classification and diagnosis of many diseases such as cancer at gene expression level. Though, numerous difficulties may affect the performance of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data preprocessing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper proposed a new technique for feature selection and classification of breast cancer based on Flower Pollination algorithm (FPA) and Support Vector machine (SVM) using microarray data. The result for this research reveals that FPA-SVM is promising by outperforming the state of the earth Particle Swam Optimization algorithm with 80.11% accuracy. Journal of Technology Management and Business 2018 Article PeerReviewed Mohamed Radzi, Nor Haizan and Salleh @ Sallehuddin, Roselina and Mustaffa, Noorfa Haszlinna and Dankolo, Muhammad Nasiru (2018) Hybrid flower pollination algorithm and support vector machine for breast cancer classification. Journal of Technology Management and Business, 5 (1). pp. 36-42. ISSN 2289-7224 https://publisher.uthm.edu.my |
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QA75 Electronic computers. Computer science Mohamed Radzi, Nor Haizan Salleh @ Sallehuddin, Roselina Mustaffa, Noorfa Haszlinna Dankolo, Muhammad Nasiru Hybrid flower pollination algorithm and support vector machine for breast cancer classification |
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Microarray technology is a system that enable experts to examine gene profile at molecular level for early disease detection. Machine learning algorithms such as classification are used in detection of dieses from data generated by microarray. It increases the potentials of classification and diagnosis of many diseases such as cancer at gene expression level. Though, numerous difficulties may affect the performance of machine learning algorithms which includes vast number of genes features comprised in the original data. Many of these features may be unrelated to the intended analysis. Therefore, feature selection is necessary to be performed in the data preprocessing. Many feature selection algorithms are developed and applied on microarray which including the metaheuristic optimization algorithms. This paper proposed a new technique for feature selection and classification of breast cancer based on Flower Pollination algorithm (FPA) and Support Vector machine (SVM) using microarray data. The result for this research reveals that FPA-SVM is promising by outperforming the state of the earth Particle Swam Optimization algorithm with 80.11% accuracy. |
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Article |
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Mohamed Radzi, Nor Haizan Salleh @ Sallehuddin, Roselina Mustaffa, Noorfa Haszlinna Dankolo, Muhammad Nasiru |
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Mohamed Radzi, Nor Haizan Salleh @ Sallehuddin, Roselina Mustaffa, Noorfa Haszlinna Dankolo, Muhammad Nasiru |
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Mohamed Radzi, Nor Haizan |
title |
Hybrid flower pollination algorithm and support vector machine for breast cancer classification |
title_short |
Hybrid flower pollination algorithm and support vector machine for breast cancer classification |
title_full |
Hybrid flower pollination algorithm and support vector machine for breast cancer classification |
title_fullStr |
Hybrid flower pollination algorithm and support vector machine for breast cancer classification |
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Hybrid flower pollination algorithm and support vector machine for breast cancer classification |
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hybrid flower pollination algorithm and support vector machine for breast cancer classification |
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Journal of Technology Management and Business |
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2018 |
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http://eprints.utm.my/id/eprint/82346/ https://publisher.uthm.edu.my |
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1654960027270643712 |
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