Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Efficient huge-scale feature selection with speciated genetic algorithm
Pattern Recognition Letters
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
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
Computers in Biology and Medicine
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
Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Group Recommender Model for Boosting and Optimizing Customer Purchases
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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The gene expression data are usually provided with a large number of genes and a relatively small number of samples, which brings a lot of new challenges. Selecting those informative genes becomes the main issue in microarray data analysis. Recursive cluster elimination based on support vector machine (SVM-RCE) has shown the better classification accuracy on some microarray data sets than recursive feature elimination based on support vector machine (SVM-RFE). However, SVM-RCE is extremely time-consuming. In this paper, we propose an improved method of SVM-RCE called ISVM-RCE. ISVM-RCE first trains a SVM model with all clusters, then applies the infinite norm of weight coefficient vector in each cluster to score the cluster, finally eliminates the gene clusters with the lowest score. In addition, ISVM-RCE eliminates genes within the clusters instead of removing a cluster of genes when the number of clusters is small. We have tested ISVM-RCE on six gene expression data sets and compared their performances with SVM-RCE and linear-discriminant-analysis-based RFE (LDA-RFE). The experiment results on these data sets show that ISVM-RCE greatly reduces the time cost of SVM-RCE, meanwhile obtains comparable classification performance as SVM-RCE, while LDA-RFE is not stable.