Multi-class classification via heterogeneous ensemble of one-class classifiers

Seokho Kang, Sungzoon Cho, Pilsung Kang

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


In this paper, a multi-class classification method based on heterogeneous ensemble of one-class classifiers is proposed. The proposed method consists of two phases: training heterogeneous one-class classifiers for each class using various one-class classification algorithms, and constructing an ensemble by combining the base classifiers using multi-response linear regression-based stacking. The use of various classification algorithms contributes towards increasing the diversity of the ensemble, while stacking resolves the normalization issues on different scales of outputs obtained from the base classifiers. In addition, we also demonstrate the selective utilization of base classifiers by adopting a stepwise variable selection procedure during stacking. Through our experiments on multi-class benchmark datasets, we concluded that our proposed method outperforms the methods that are based on single one-class classification algorithms with statistical significance.

Original languageEnglish
Pages (from-to)35-43
Number of pages9
JournalEngineering Applications of Artificial Intelligence
Publication statusPublished - 2015 Aug 1


  • Ensemble
  • Heterogeneous ensemble
  • Meta-learning
  • Multi-class classification
  • One-class classification
  • Stacking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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