Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

Arowolo, Michael Olaolu and Adebiyi, Marion and Adebiyi, A. A. and Olugbara, Oludayo (2021) Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier. Journal of Big Data, 8 (1). ISSN 2196-1115

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Abstract

RNA-Seq data are utilized for biological applications and decision making for the clas�sifcation of genes. A lot of works in recent time are focused on reducing the dimen�sion of RNA-Seq data. Dimensionality reduction approaches have been proposed in the transformation of these data. In this study, a novel optimized hybrid investigative approach is proposed. It combines an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respec�tively. The classifer uses KNN on the reduced mosquito Anopheles gambiae dataset, to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based on the high-dimensional input feature space. A fast algorithm for feature ranking is used to select relevant features. The performances of the model are evaluated and validated using the classifcation accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for selecting relevant genes and classifying pertinent gene expression data analysis by indicating that the approach is capable of adding to prevailing machine learning methods.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User:	Mr Uchechukwu F. Ekpendu
Date Deposited:	02 Jul 2021 10:49
Last Modified:	02 Jul 2021 10:49
URI:	https://eprints.lmu.edu.ng/id/eprint/3155

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