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Graph-based iterative hybrid feature selection.
| Content Provider | CiteSeerX |
|---|---|
| Author | Zhong, Erheng Xie, Sihong Fan, Wei Ren, Jiangtao Peng, Jing Zhang, Kun |
| Abstract | When the number of labeled examples is limited, traditional supervised feature selection techniques often fail due to sample selection bias or unrepresentative sample problem. To solve this, semi-supervised feature selection techniques exploit the statistical information of both labeled and unlabeled examples in the same time. However, the results of semi-supervised feature selection can be at times unsatisfactory, and the culprit is on how to effectively use the unlabeled data. Quite different from both supervised and semi-supervised feature selection, we propose a “hybrid” framework based on graph models. We first apply supervised methods to select a small set of most critical features from the labeled data. Importantly, these initial features might otherwise be missed when selection is performed on the labeled and unlabeled examples simultaneously. Next, this initial feature set is expanded and corrected with the use of unlabeled data. We formally analyze why the expected performance of the hybrid framework is better than both supervised and semi-supervised feature selection. Experimental results demonstrate that the proposed method outperforms both traditional supervised and state-of-the-art semisupervised feature selection algorithms by at least 10 % in accuracy on a number of text and biomedical problems with thousands of features to choose from. Software and dataset is available from the authors. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Semi-supervised Feature Selection Graph-based Iterative Hybrid Feature Selection Initial Feature Unlabeled Data Unlabeled Example Hybrid Framework Supervised Method Biomedical Problem Unrepresentative Sample Problem Small Set Semi-supervised Feature Selection Technique Critical Feature Traditional Supervised Feature Selection Technique Statistical Information State-of-the-art Semisupervised Feature Selection Algorithm Expected Performance Graph Model Experimental Result Selection Bias Labeled Example |
| Content Type | Text |
