Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11446
Title: Multi-view based adaboost classifier ensemble for class prediction from gene expression profiles
Authors: Li, L
Yu, Z
Liu, J
You, J 
Wong, HS
Han, G
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - International Conference on Pattern Recognition, 2014, 6976751, p. 178-183 How to cite?
Abstract: 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.
Description: 22nd International Conference on Pattern Recognition, ICPR 2014, Sweden, 24-28 August 2014
URI: http://hdl.handle.net/10397/11446
ISBN: 978-1-4799-5209-0 (electronic)
978-1-4799-5210-6 (print on demand (PoD))
ISSN: 1051-4651
DOI: 10.1109/ICPR.2014.40
Appears in Collections:Conference Paper

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