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Multi-objective optimization of feature selection using hybrid cat swarm optimization

Science China(Technological Sciences) 2021年第3期64卷 508-520页

作者： GAO Xiao-Zhi nalluri madhu sudana rao KANNAN K SINHAROY Diptendu School of Computing University of Eastern FinlandKuopio 70210Finland School of Engineering Department of MathematicsAmrita Vishwa VidyapeethamCoimbator 641112India School of Humanities and Sciences SASTRA UniversityThanjavur 612001India Department of Computer Science and Engineering National Institute of TechnologyMeghalayaShillong 793003India

With the pervasive generation of information from a wide range of sensors and devices,there always exist a large number of input features in databases,thus complicating machine learning problem ***,certain features are relatively impertinent to specific problems,which may degrade the performances of classifiers in terms of prediction accuracy,sensitivity,specificity,and recall *** main goal of a multi-objective optimization problem is to identify the subsets of the given *** this end,a hybrid cat swarm optimization(HCSO)algorithm is proposed in our paper for performance improvement of the basic cat swarm optimization(CSO)that incorporates guided and competitive&inherent characteristics into the original *** performance of HCSO has been tested by finding the optimal feature subset for 15 benchmark *** number of class labels for these datasets varies between 2 and *** time complexity analysis of both CSO and HCSO has also been ***,the performance of the proposed algorithm has been compared with that of simple CSO and other state-ofthe-art *** performances obtained by HCSO have an average 2.68%improvement with a standard deviation of *** maximum performance improvement is up to 10.09%in prediction *** on the same datasets,CSO has yielded improvements within the range of-7.27%to 8.51%with an average improvement 0.9%and standard deviation *** statistical tests carried out in the experiments prove that HCSO manifests a moderately better feature selection capacity than that of its counterparts.

关键词： feature selection competition cat swarm optimization guided search parameter evolution

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