ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Bioinformatics
Fast Gene Selection for Microarray Data Using SVM-Based Evaluation Criterion
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
Classification of microarrays with kNN: comparison of dimensionality reduction methods
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A modified two-stage SVM-RFE model for cancer classification using microarray data
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
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Compared to backward feature selection (BFS) method in gene expression data analysis, forward feature selection (FFS) method can obtain an expected feature subset with less iteration. However, the number of FFS method is considerably less than that of BFS method. More efficient FFS methods need to be developed. In this paper, two FFS methods based on the pruning of the classifier ensembles generated by single attribute are proposed for gene selection. The main contributions are as follows: (1) a new loss function, p-insensitive loss function, is proposed to overcome the disadvantage of the margin Euclidean distance loss function in the pruning of classifier ensembles; (2) two FFS methods based on the margin Euclidean distance loss function and the p-insensitive loss function, named as FFS-ACSA1 and FFS-ACSA2 respectively, are proposed; (3) the comparison experiments on four gene expression datasets show that FFS-ACSA2 obtains the best results among three FFS methods (i.e. signal-to-noise ratio (SNR), FFS-ACSA1 and FFS-ACSA2), and is competitive to the famous support vector machine-based recursive feature elimination (SVM-RFE), while FFS-ACSA1 is unstable.