TY - GEN
T1 - Enhanced recursive feature elimination
AU - Chen, Xue Wen
AU - Jeong, Jong Cheol
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=47349084672&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47349084672&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2007.44
DO - 10.1109/ICMLA.2007.44
M3 - Conference contribution
AN - SCOPUS:47349084672
SN - 0769530699
SN - 9780769530697
T3 - Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007
SP - 429
EP - 435
BT - Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007
T2 - 6th International Conference on Machine Learning and Applications, ICMLA 2007
Y2 - 13 December 2007 through 15 December 2007
ER -