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Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection

Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection

Audu Musa Mabu, Rajesh Prasad, Raghav Yadav
Copyright: © 2020 |Volume: 11 |Issue: 1 |Pages: 22
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781799806691|DOI: 10.4018/IJSIR.2020010104
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MLA

Mabu, Audu Musa, et al. "Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection." IJSIR vol.11, no.1 2020: pp.65-86. http://doi.org/10.4018/IJSIR.2020010104

APA

Mabu, A. M., Prasad, R., & Yadav, R. (2020). Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection. International Journal of Swarm Intelligence Research (IJSIR), 11(1), 65-86. http://doi.org/10.4018/IJSIR.2020010104

Chicago

Mabu, Audu Musa, Rajesh Prasad, and Raghav Yadav. "Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection," International Journal of Swarm Intelligence Research (IJSIR) 11, no.1: 65-86. http://doi.org/10.4018/IJSIR.2020010104

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Abstract

With the progression of bioinformatics, applications of GE profiles on cancer diagnosis along with classification have become an intriguing subject in the bioinformatics field. It holds numerous genes with few samples that make it arduous to examine and process. A novel strategy aimed at the classification of GE dataset as well as clustering-centered feature selection is proposed in the paper. The proposed technique first preprocesses the dataset using normalization, and later, feature selection was accomplished with the assistance of feature clustering support vector machine (FCSVM). It has two phases, gene clustering and gene representation. To make the chose top-positioned features worthy for classification, feature reduction is performed by utilizing SVM-recursive feature elimination (SVM-RFE) algorithm. Finally, the feature-reduced data set was classified using artificial neural network (ANN) classifier. When compared with some recent swarm intelligence feature reduction approach, FCSVM-ANN showed an elegant performance.

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