A twin-hyperellipsoidal support vector classifier

Issue title: Special Section: Green and Human Information Technology

Guest editors: Seong Oun Hwang

Article type: Research Article

Authors: Teeyapan, Kasemsit^{a; b} | Theera-Umpon, Nipon^{a; c; *} | Auephanwiriyakul, Sansanee^{c; d}

Affiliations: [a] Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand | [b] Graduate School, Chiang Mai University, Chiang Mai, Thailand | [c] Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand | [d] Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand

Correspondence: [*] Corresponding author. Nipon Theera-Umpon, Tel.: +66-5394-2083; E-mail: [email protected].

Abstract: This paper presents a novel binary classifier based on two best fitting hyperellipsoids in the feature space, called twin-hyperellipsoidal support vector machine (TESVM). The idea of TESVM is inspired by the minimum volume covering ellipsoid together with twin-hypersphere support vector machine (THSVM) which is a variant of the well-known support vector data description (SVDD). Following the concept of THSVM, TESVM constructs two hyperellipsoids where each hyperellipsoid is closest to one class but also as far as possible from the other class in order to form a decision boundary. The construction of hyperellipsoids in the feature space is also enabled through the use of empirical feature mapping. The experimental results on several artificial as well as standard real-world datasets are provided to demonstrate the performance of TESVM. Particularly, TESVM outperforms its spherical counterpart in term of classification accuracy.

Keywords: Kernel minimum volume covering ellipsoid, twin-hyperellipsoid, twin hypersphere, empirical feature mapping

DOI: 10.3233/JIFS-169854

Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6141-6152, 2018