Advances in neural information processing systems 2
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Principles of data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Microarray data mining: facing the challenges
ACM SIGKDD Explorations Newsletter
Machine learning methods applied to DNA microarray data can improve the diagnosis of cancer
ACM SIGKDD Explorations Newsletter
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Biological pathways as features for microarray data classification
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
Gene boosting for cancer classification based on gene expression profiles
Pattern Recognition
Journal of Biomedical Informatics
A Neural Approach for SME's Credit Risk Analysis in Turkey
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Incremental Bayesian Network Learning for Scalable Feature Selection
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Tumor clustering using nonnegative matrix factorization with gene selection
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Artificial Intelligence in Medicine
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Recursive Mahalanobis Separability Measure for Gene Subset Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
SVM-RFE based feature selection for tandem mass spectrum quality assessment
International Journal of Data Mining and Bioinformatics
Knowledge discovery using neural approach for SME's credit risk analysis problem in Turkey
Expert Systems with Applications: An International Journal
Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Expert Systems with Applications: An International Journal
Feature selection for support vector machines with RBF kernel
Artificial Intelligence Review
Sparse and stable gene selection with consensus SVM-RFE
Pattern Recognition Letters
Stable Gene Selection from Microarray Data via Sample Weighting
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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)
A novel multi-stage feature selection method for microarray expression data analysis
International Journal of Data Mining and Bioinformatics
Computers in Biology and Medicine
Simultaneous sample and gene selection using t-score and approximate support vectors
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Automatic feature selection for named entity recognition using genetic algorithm
Proceedings of the Fourth Symposium on Information and Communication Technology
Robust feature selection based on regularized brownboost loss
Knowledge-Based Systems
PLS-based recursive feature elimination for high-dimensional small sample
Knowledge-Based Systems
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Extracting a subset of informative genes from microarray expression data is a critical data preparation step in cancer classification and other biological function analyses. Though many algorithms have been developed, the Support Vector Machine - Recursive Feature Elimination (SVM-RFE) algorithm is one of the best gene feature selection algorithms. It assumes that a smaller "filter-out" factor in the SVM-RFE, which results in a smaller number of gene features eliminated in each recursion, should lead to extraction of a better gene subset. Because the SVM-RFE is highly sensitive to the "filter-out" factor, our simulations have shown that this assumption is not always correct and that the SVM-RFE is an unstable algorithm. To select a set of key gene features for reliable prediction of cancer types or subtypes and other applications, a new two-stage SVM-RFE algorithm has been developed. It is designed to effectively eliminate most of the irrelevant, redundant and noisy genes while keeping information loss small at the first stage. A fine selection for the final gene subset is then performed at the second stage. The two-stage SVM-RFE overcomes the instability problem of the SVM-RFE to achieve better algorithm utility. We have demonstrated that the two-stage SVM-RFE is significantly more accurate and more reliable than the SVM-RFE and three correlation-based methods based on our analysis of three publicly available microarray expression datasets. Furthermore, the two-stage SVM-RFE is computationally efficient because its time complexity is $O(d * \log{_2d})$, where $d$ is the size of the original gene set.