Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Support Vector Machines with L1 penalty for detecting gene-gene interactions
International Journal of Data Mining and Bioinformatics
Predicting linear B-cell epitopes by using sequence-derived structural and physicochemical features
International Journal of Data Mining and Bioinformatics
International Journal of Data Mining and Bioinformatics
A multi-index ROC-based methodology for high throughput experiments in gene discovery
International Journal of Data Mining and Bioinformatics
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Feature selection is effective in selecting predictive gene sets for microarray classification. However, the large number of predictive gene sets and the disparity among them presents a challenge for identifying potential biomarkers. To facilitate biomarker identification, we present a new data mining task, feature cluster selection, which selects from a full set of features a small number of coherent and predictive feature clusters. We provide both theoretical definition and empirical formulation for the new problem, and propose an efficient 3M algorithm. Experiments on microarray data have shown that the 3M algorithm can select predictive and statistically significant gene clusters.