Title: A hybridised feature selection approach in molecular classification using CSO and GA

Authors: Ahmed Elsawy; Mazen M. Selim; Mahmoud Sobhy

Addresses: Computer Science Department, Faculty of Computers and Informatics, Benha University, Benha, Qalyubia Governorate, Egypt ' Computer Science Department, Faculty of Computers and Informatics, Benha University, Benha, Qalyubia Governorate, Egypt ' Computer Science Department, Faculty of Computers and Informatics, Benha University, Benha, Qalyubia Governorate, Egypt

Abstract: Feature selection in molecular classification is a basic area of research in chemoinformatics field. This paper introduces a hybrid approach that investigates the performances of chicken swarm optimisation (CSO) algorithm with genetic algorithms (GA) for feature selection and support vector machine (SVM) for classification. The purpose of this paper is to test the effect of elimination of the inconsequential and redundant features in chemical datasets to realise the success of the classification. The proposed algorithm was applied to four chemical datasets and proved superiority in achieving minimum classification error rate in comparison with different feature selection algorithms for molecular classification.

Keywords: molecular classification; chicken swarm optimisation; genetic algorithms; support vector machines; feature selection.

DOI: 10.1504/IJCAT.2019.098034

International Journal of Computer Applications in Technology, 2019 Vol.59 No.2, pp.165 - 174

Received: 12 Feb 2018
Accepted: 06 May 2018
Published online: 27 Feb 2019 *

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