A hybridised feature selection approach in molecular classification using CSO and GA. (2019)

• Record Type: Journal Article
• Authors: Elsawy, Ahmed; Selim, Mazen M.; Sobhy, Mahmoud
• Is Part Of: International journal of computer applications technology. Volume 59:Number 2(2019)
• Page Start: 165
• Page End: 174
• Publication Date: 2019
• Subjects: molecular classification -- chicken swarm optimisation -- genetic algorithms -- support vector machines -- feature selection; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

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.