Transductive polyhedral conic classifiers for machine learning applications

ÇEVİKALP H., Saglamlar H.

Pattern Recognition Letters, vol.161, pp.1-7, 2022 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 161
  • Publication Date: 2022
  • Doi Number: 10.1016/j.patrec.2022.07.001
  • Journal Name: Pattern Recognition Letters
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.1-7
  • Keywords: Transductive learning, Polyhedral conic classifier, Large -margin classifier, Optimization
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

© 2022 Elsevier B.V.In this paper, we introduce novel methods called Transductive Polyhedral Conic Classifiers that use both labeled and unlabeled data for classification. The proposed methodology utilizes the concave-convex procedure to solve the resulting optimization problems as in the Robust Transductive Support Vector Machines (RTSVMs). However, unlike RTSVM that uses SVM formulation, our proposed methods use the polyhedral conic classifier formulation that returns tight and closed decision boundaries compared to SVM. We tested the proposed methods on various datasets and experimental results show that our proposed methods yield better results than the existing transductive learning classifiers.