Improving classification of microarray data using prototype-based feature selection
ACM SIGKDD Explorations Newsletter
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Pattern classification in DNA microarray data of multiple tumor types
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Cancer gene search with data-mining and genetic algorithms
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Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Bioinformatics
Gene Selection Using Neighborhood Rough Set from Gene Expression Profiles
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A review of feature selection techniques in bioinformatics
Bioinformatics
Bioinformatics
Optimal Search-Based Gene Subset Selection for Gene Array Cancer Classification
IEEE Transactions on Information Technology in Biomedicine
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Selecting a small number of discriminative genes from thousands is a fundamental task in microarray data analysis. An effective feature selection allows biologists to investigate only a subset of genes instead of the entire set, thus avoiding insignificant, noisy, and redundant features. This paper presents the MaskedPainter feature selection method for gene expression data. The proposed method measures the ability of each gene to classify samples belonging to different classes and ranks genes by computing an overlap score. A density based technique is exploited to smooth the effects of outliers in the overlap score computation. Analogously to other approaches, the number of selected genes can be set by the user. However, our algorithm may automatically detect the minimum set of genes that yields the best classification coverage of training set samples. The effectiveness of our approach has been demonstrated through an empirical study on public microarray datasets with different characteristics. Experimental results show that the proposed approach yields a higher classification accuracy with respect to widely used feature selection techniques.