Semantic Partition Based Association Rule Mining across Multiple Databases Using Abstraction

Abstract

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.