Semi-supervised ensemble classification in subspaces

Guoxian Yu, Guoji Zhang, Zhiwen Yu, Carlotta Domeniconi, Jia You, Guoqiang Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)


Graph-based semi-supervised classification depends on a well-structured graph. However, it is difficult to construct a graph that faithfully reflects the underlying structure of data distribution, especially for data with a high dimensional representation. In this paper, we focus on graph construction and propose a novel method called semi-supervised ensemble classification in subspaces, SSEC in short. Unlike traditional methods that execute graph-based semi-supervised classification in the original space, SSEC performs semi-supervised linear classification in subspaces. More specifically, SSEC first divides the original feature space into several disjoint feature subspaces. Then, it constructs a neighborhood graph in each subspace, and trains a semi-supervised linear classifier on this graph, which will serve as the base classifier in an ensemble. Finally, SSEC combines the obtained base classifiers into an ensemble classifier using the majority-voting rule. Experimental results on facial images classification show that SSEC not only has higher classification accuracy than the competitive methods, but also can be effective in a wide range of values of input parameters.
Original languageEnglish
Pages (from-to)1511-1522
Number of pages12
JournalApplied Soft Computing Journal
Issue number5
Publication statusPublished - 1 May 2012


  • Ensemble classification
  • Graph construction
  • High dimensional data
  • Semi-supervised classification
  • Subspaces

ASJC Scopus subject areas

  • Software


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