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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Enas Mohammed Hussein Saeed
Al-Mustansiriya University
Computer Science & IT

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Hybrid features selection method using random forest and meerkat clan algorithm Hayder Adnan Saleh; Rana Amer Sattar; Enas Mohammed Hussein Saeed; Dalael Saad Abdul-Zahra
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 5: October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i5.23515

Abstract

In the majority of gene expression investigations, selecting relevant genes for sample classification is considered a frequent challenge, with researchers attempting to discover the minimum feasible number of genes while yet achieving excellent predictive performance. Various gene selection methods employ univariate (gene-by-gene) gene relevance rankings as well as arbitrary thresholds for selecting the number of genes, are only applicable to 2-class problems and use gene selection ranking criteria unrelated to the algorithm of classification. A modified random forest (MRF) algorithm depending on the meerkat clan algorithm (MCA) is provided in this work. It is one of the swarm intelligence algorithms and one of the most significant machine learning approaches in the decision tree. MCA is used to choose characteristics for the RF algorithm. In information systems, databases, and other applications, feature selection imputation is critical. The proposed algorithm was applied to three different databases, where the experimental results for accuracy and time proved the superiority of the proposed algorithm over the original algorithm.
Co-Authors Dalael Saad Abdul-Zahra Hayder Adnan Saleh Rana Amer Sattar