Stepwise feature selection using generalized logistic loss
Computational Statistics & Data Analysis
F-score with Pareto Front Analysis for Multiclass Gene Selection
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
Sparse Support Vector Machines with L_{p} Penalty for Biomarker Identification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An improved approach to steganalysis of JPEG images
Information Sciences: an International Journal
Ensemble gene selection for cancer classification
Pattern Recognition
A novel method to robust tumor classification based on MACE filter
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A novel metric for redundant gene elimination based on discriminative contribution
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
A novel hybrid feature selection method for microarray data analysis
Applied Soft Computing
The Journal of Machine Learning Research
Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SVM based feature selection: why are we using the dual?
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
A gene selection method for microarray data based on sampling
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Computers in Biology and Medicine
Sparse and stable gene selection with consensus SVM-RFE
Pattern Recognition Letters
Constructing gene regulatory networks from microarray data using GA/PSO with DTW
Applied Soft Computing
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
The classification of cancer stage microarray data
Computer Methods and Programs in Biomedicine
Assessing similarity of feature selection techniques in high-dimensional domains
Pattern Recognition Letters
Robust feature selection based on regularized brownboost loss
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
PLS-based recursive feature elimination for high-dimensional small sample
Knowledge-Based Systems
Feature selection with SVD entropy: Some modification and extension
Information Sciences: an International Journal
MaskedPainter: Feature selection for microarray data analysis
Intelligent Data Analysis
Diverse accurate feature selection for microarray cancer diagnosis
Intelligent Data Analysis
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Motivation: Given the thousands of genes and the small number of samples, gene selection has emerged as an important research problem in microarray data analysis. Support Vector Machine—Recursive Feature Elimination (SVM-RFE) is one of a group of recently described algorithms which represent the stat-of-the-art for gene selection. Just like SVM itself, SVM-RFE was originally designed to solve binary gene selection problems. Several groups have extended SVM-RFE to solve multiclass problems using one-versus-all techniques. However, the genes selected from one binary gene selection problem may reduce the classification performance in other binary problems. Results: In the present study, we propose a family of four extensions to SVM-RFE (called MSVM-RFE) to solve the multiclass gene selection problem, based on different frameworks of multiclass SVMs. By simultaneously considering all classes during the gene selection stages, our proposed extensions identify genes leading to more accurate classification. Contact: david.tuck@yale.edu Supplementary information: Supplementary materials, including a detailed review of both binary and multiclass SVMs, and complete experimental results, are available at Bioinformatics online.