Progressive subspace ensemble learning

Zhiwen Yu, Daxing Wang, Jia You, Hau San Wong, Si Wu, Jun Zhang, Guoqiang Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)


For example, the AdaBoost approach only investigates the data sample space, while the random subspace technique only focuses on the feature space. To address this limitation, we propose the progressive subspace ensemble learning approach (PSEL) which takes into account the data sample space and the feature space at the same time. Specifically, PSEL first adopts the random subspace technique to generate a set of subspaces. Then, a progressive selection process based on new cost functions that incorporate current and long-term information to select the classifiers sequentially will be introduced. Finally, a weighted voting scheme is used to summarize the predicted labels and obtain the final result. We also adopt a number of non-parametric tests to compare PSEL and its competitors over multiple datasets. The results of the experiments show that PSEL works well on most of the real datasets, and outperforms a number of state-of-the-art classifier ensemble approaches.
Original languageEnglish
Pages (from-to)692-705
Number of pages14
JournalPattern Recognition
Publication statusPublished - 1 Dec 2016


  • AdaBoost
  • Classifier ensemble
  • Decision tree
  • Ensemble learning
  • Random subspace

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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