期刊
KNOWLEDGE AND INFORMATION SYSTEMS
卷 49, 期 3, 页码 1161-1185出版社
SPRINGER LONDON LTD
DOI: 10.1007/s10115-015-0901-0
关键词
Feature selection; Semi-supervised context; Ensemble methods; Constraints
In this paper, we propose an efficient and robust approach for semi-supervised feature selection, based on the constrained Laplacian score. The main drawback of this method is the choice of the scant supervision information, represented by pairwise constraints. In fact, constraints are proven to have some noise which may deteriorate learning performance. In this work, we try to override any negative effects of constraint set by the variation of their sources. This is achieved by an ensemble technique using both a resampling of data (bagging) and a random subspace strategy. Experiments on high-dimensional datasets are provided for validating the proposed approach and comparing it with other representative feature selection methods.
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