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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1002/maco.201106107

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1433-3058

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A multiverse optimizer approach for feature selec

Authors: Hossam Faris Mohammad A Hassonah Ala’ M AlZoubi Seyedali Mirjalili Ibrahim Aljarah
Publish Date: 2017/01/02
Volume: 30, Issue: 8, Pages: 2355-2369
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Abstract

Support vector machine SVM is a wellregarded machine learning algorithm widely applied to classification tasks and regression problems SVM was founded based on the statistical learning theory and structural risk minimization Despite the high prediction rate of this technique in a wide range of real applications the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection This work proposes a robust approach based on a recent natureinspired metaheuristic called multiverse optimizer MVO for selecting optimal features and optimizing the parameters of SVM simultaneously In fact the MVO algorithm is employed as a tuner to manipulate the main parameters of SVM and find the optimal set of features for this classifier The proposed approach is implemented and tested on two different system architectures MVO is benchmarked and compared with four classic and recent metaheuristic algorithms using ten binary and multiclass labeled datasets Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy

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