Enhanced recursive feature elimination

For classification with small training samples and high dimensionality, feature selection plays an important role in avoiding overfitting problems and improving classification performance. One of the commonly used feature selection methods for small samples problems is recursive feature elimination...

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Bibliographic Details
Main Authors: Xue-wen Chen, Jong Cheol Jeong
Format: Conference Proceeding
Language:English
Subjects:
Application software
Cancer
Degradation
Filters
Hyperspectral sensors
Machine learning
Support vector machine classification
Support vector machines
Target recognition
Training data
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Summary:For classification with small training samples and high dimensionality, feature selection plays an important role in avoiding overfitting problems and improving classification performance. One of the commonly used feature selection methods for small samples problems is recursive feature elimination (RFE) method. RFE method utilizes the generalization capability embedded in support vector machines and is thus suitable for small samples problems. Despite its good performance, RFE tends to discard "weak" features, which may provide a significant improvement of performance when combined with other features. In this paper, we propose an enhanced recursive feature elimination (EnRFE) method for feature selection in small training sample classification. Our experimental results show that the proposed method outperforms the original RFE in terms of classification accuracy on various datasets.
DOI:10.1109/ICMLA.2007.35