Multi-objective symbiotic organism search algorithm for optimal feature selection in brain computer interfaces. (1st March 2021)

• Record Type: Journal Article
• Authors: Baysal, Yesim A.; Ketenci, Seniha; Altas, Ismail H.; Kayikcioglu, Temel
• Is Part Of: Expert systems with applications. Volume 165(2021)
• Publication Date: 2021-03-01
• Subjects: Feature selection -- Symbiotic search organism algorithm -- Multi-objective optimization -- Brain computer interface; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113907
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Feature selection algorithms based SOS algorithm are presented. Four different algorithms are improved in order to select of optimal features. MOSOS algorithm is the best to maximize accuracy while minimizing feature number. Performance of the improved algorithms is evaluated on two well-known datasets. Abstract: Feature selection is crucial to develop a brain computer interface (BCI) system which has high classification accuracy and less computational complexity in especially a large feature space. Feature selection (FS) problem has been solved by many various methods. Among these methods, especially evolutionary computation (EC) techniques have gained a lot of attention in recent years. However, there are very few studies in the literature that consider FS problem as a multi-objective problem to find the optimal trade-off between classification accuracy and the number of selected features. Therefore, in this paper, a non-dominated sorting multi-objective symbiotic organism search (NSMOSOS) algorithm is proposed to generate the optimal feature subset in BCI. The efficiency and robustness of the proposed algorithm as a feature selection method is investigated in two datasets based on motor imagery. The highest classification accuracies of NSMOSOS for dataset 1 and dataset 2 are obtained 97.86% with 11 features and 96.57% with average 19 features, respectively. The obtained results demonstrate that the proposed method achieves satisfying results with regard to both the classification accuracy improvement and feature reduction rates for both datasets. The superiority of the proposed method is verified compared with the existing methods for both datasets. Besides, three different versions of symbiotic search organism (SOS) algorithm are improved, and pros and cons of these algorithms are evaluated compared with each other. In conclusion, the paper indicates that the proposed NSMOSOS algorithm is an efficient and practicable technique for FS problem and could be helpful in developing the BCI applications.