Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Extraction of informative genes from microarray data
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
A hybrid feature selection approach for microarray gene expression data
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A hybrid model to favor the selection of high quality features in high dimensional domains
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Robust Classification Method of Tumor Subtype by Using Correlation Filters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Identifying a small set of marker genes using minimum expected cost of misclassification
Artificial Intelligence in Medicine
Assessing similarity of feature selection techniques in high-dimensional domains
Pattern Recognition Letters
Journal of Biomedical Informatics
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Filters and wrappers are two prevailing approaches for gene selection in microarray data analysis. Filters make use of statistical properties of each gene to represent its discriminating power between different classes. The computation is fast but the predictions are inaccurate. Wrappers make use of a chosen classifier to select genes by maximizing classification accuracy, but the computation burden is formidable. Filters and wrappers have been combined in previous studies to maximize the classification accuracy for a chosen classifier with respect to a filtered set of genes. The drawback of this single-filter-single-wrapper (SFSW) approach is that the classification accuracy is dependent on the choice of specific filter and wrapper. In this paper, a multiple-filter-multiple-wrapper (MFMW) approach is proposed that makes use of multiple filters and multiple wrappers to improve the accuracy and robustness of the classification, and to identify potential biomarker genes. Experiments based on six benchmark data sets show that the MFMW approach outperforms SFSW models (generated by all combinations of filters and wrappers used in the corresponding MFMW model) in all cases and for all six data sets. Some of MFMW-selected genes have been confirmed to be biomarkers or contribute to the development of particular cancers by other studies.