Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized rough sets based feature selection
Intelligent Data Analysis
Fuzzy-rough attribute reduction via mutual information with an application to cancer classification
Computers & Mathematics with Applications
An Efficient Gene Selection Algorithm Based on Tolerance Rough Set Theory
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Rough set based approaches to feature selection for Case-Based Reasoning classifiers
Pattern Recognition Letters
Evolutionary tolerance-based gene selection in gene expression data
Transactions on rough sets XIV
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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The generic approach to cancer classification based on gene expression data is important for accurate cancer diagnosis, instead of using all genes in the dataset, we select a small gene subset out of thousands of genes for classification. Rough set theory is a tool for reducing redundancy in information systems, thus Application of Rough Set to gene selection is interesting. In this paper, a novel gene selection method called RMIMR is proposed for gene selection, which searches for the subset through maximum relevance and maximum positive interaction of genes. Compared with the classical methods based on statistics,information theory and regression, Our method leads to significantly improved classification in experiments on 4 gene expression datasets