A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture

Hossam Faris, Mohammad A. Hassonah, Ala’ M. Al-Zoubi, Seyedali Mirjalili, Ibrahim Aljarah

Research output: Contribution to journalArticlepeer-review

69 Citations (Scopus)

Abstract

Support vector machine (SVM) is a well-regarded 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 nature-inspired metaheuristic called multi-verse 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 multi-class labeled datasets. Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy.

Original languageEnglish
Pages (from-to)2355-2369
Number of pages15
JournalNeural Computing and Applications
Volume30
Issue number8
DOIs
Publication statusPublished - 1 Oct 2018
Externally publishedYes

Keywords

  • Feature selection
  • Metaheuristics
  • Multi-verse optimizer
  • MVO
  • Optimization
  • Support vector machines
  • SVM

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