Co-training with relevant random subspaces

We introduce the relevant random subspace Co-training (Rel-RASCO) algorithm which produces relevant random subspaces and then does semi-supervised ensemble learning using those subspaces and unlabeled data. Ensemble learning algorithms may benefit from diversity of classifiers used. However, for hig...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2010-06, Vol.73 (10), p.1652-1661
Main Authors: Yaslan, Yusuf, Cataltepe, Zehra
Format: Article
Language:English
Subjects:
Co-training
Multiple classifier systems
Random subspace methods
RASCO
Relevant subspace method
Semi-supervised learning
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Summary:We introduce the relevant random subspace Co-training (Rel-RASCO) algorithm which produces relevant random subspaces and then does semi-supervised ensemble learning using those subspaces and unlabeled data. Ensemble learning algorithms may benefit from diversity of classifiers used. However, for high dimensional data choosing subspaces randomly, as in RASCO (Random Subspace Method for Co-training, Wang et al. 2008 [5]) algorithm, may produce diverse but inaccurate classifiers. We produce relevant random subspaces by means of drawing features with probabilities proportional to their relevances measured by the mutual information between features and class labels. We show that Rel-RASCO achieves better accuracy by this relevant and random subspace selection scheme. Experiments on five real and one synthetic datasets show that Rel-RASCO algorithm outperforms both RASCO and Co-training in terms of the accuracy achieved at the end of Co-training.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2010.01.018