The use of support vector machines in semi-supervised classification

Hyunjoo Bae, Hyungwoo Kim, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review

Abstract

Semi-supervised learning has gained significant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but effective algorithm for semi-supervised classification using support vector machines (SVM), one of the most popular binary classifiers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classification rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classification.

Original languageEnglish
Pages (from-to)193-202
Number of pages10
JournalCommunications for Statistical Applications and Methods
Volume29
Issue number2
DOIs
Publication statusPublished - 2022 Mar

Bibliographical note

Funding Information:
This work is partially funded by the National Research Foundation of Korea (NRF) grants 2018R1D1 A1B07043034 and 2019R1A4A1028134, and by Korea University grant K2105791.

Funding Information:
This work is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2018R1D1A1B07043034 and NRF-2019R1A4A1028134) and Korea University (Grant No. K2105791). This work is partially funded by the National Research Foundation of Korea (NRF) grants 2018R1D1 A1B07043034 and 2019R1A4A1028134, and by Korea University grant K2105791.

Funding Information:
This work is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. NRF-2018R1D1A1B07043034 and NRF-2019R1A4A1028134) and Korea University (Grant No. K2105791). 1Corresponding author: Department of Statistics, Korea University, 145 Anam-Ro, Sungbuk-Gu, Seoul 02841, Korea. E-mail: sjshin@korea.ac.kr

Publisher Copyright:
© 2022. The Korean Statistical Society, and Korean International Statistical Society. All rights reserved

Keywords

  • Dimension reduction
  • k-means clustering
  • semi-supervised classification
  • support vector machines

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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