Multi-view based adaboost classifier ensemble for class prediction from gene expression profiles

Le Li, Zhiwen Yu, Jiming Liu, Jia You, Hau San Wong, Guoqiang Han

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Multi-view learning, one of the important sub-fields in the area of machine learning, has gained more and more attention in class prediction of gene expression datasets. In this paper, we propose a new classifier ensemble framework, named as multi-view based Ad-a boost classifier ensemble framework (MV-ACE), which not only utilizes a random view generation technique to regulate different views and applies adaboost to adjust the training set, but also designs an adaptive process which explores the feasible combination of multiple views through an optimization process. Traditional multi-view learning focuses on exploring diverse views and the best integration of multiple views in a straight-forward manner, such as the linear combination of different views. Our proposed model, however, additionally applies a progressive training approach to improve the accuracies of the base classifiers. Moreover, we investigate the assembly of views at the model level, and employ an adaptive process to optimize the multi-view learning model to improve its performance. Our experiments on 12 cancer gene data sets for the classification task show that(i) MV-ACE works well on a diverse class of cancer gene expression profiles. (ii) It outperforms most of the state-of-the-art classifier ensemble approaches on these datasets.
Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
Number of pages6
ISBN (Electronic)9781479952083
Publication statusPublished - 1 Jan 2014
Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
Duration: 24 Aug 201428 Aug 2014


Conference22nd International Conference on Pattern Recognition, ICPR 2014

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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