Elements of information theory
Elements of information theory
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Markov blanket-embedded genetic algorithm for gene selection
Pattern Recognition
Ensemble methods for classification of patients for personalized medicine with high-dimensional data
Artificial Intelligence in Medicine
Bioinformatics
Techniques for clustering gene expression data
Computers in Biology and Medicine
A review of feature selection techniques in bioinformatics
Bioinformatics
Microarray-based classification and clinical predictors
Bioinformatics
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
APPLYING DATA MINING TECHNIQUES FOR CANCER CLASSIFICATION ON GENE EXPRESSION DATA
Cybernetics and Systems
New gene selection method for multiclass tumor classification by class centroid
Journal of Biomedical Informatics
Artificial Intelligence in Medicine
Feature selection with dynamic mutual information
Pattern Recognition
Impact of error estimation on feature selection
Pattern Recognition
An efficient statistical feature selection approach for classification of gene expression data
Journal of Biomedical Informatics
An effective feature selection method using dynamic information criterion
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
A new gene selection method based on random subspace ensemble for microarray cancer classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Gene selection and PSO-BP classifier encoding a prior information
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Merge method for shape-based clustering in time series microarray analysis
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Investigating Topic Models' Capabilities in Expression Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis
Journal of Biomedical Informatics
Algorithms for discovery of multiple Markov boundaries
The Journal of Machine Learning Research
Journal of Biomedical Informatics
Diverse accurate feature selection for microarray cancer diagnosis
Intelligent Data Analysis
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Selecting relevant and discriminative genes for sample classification is a common and critical task in gene expression analysis (e.g. disease diagnostic). It is desirable that gene selection can improve classification performance of learning algorithm effectively. In general, for most gene selection methods widely used in reality, an individual gene subset will be chosen according to its discriminative power. One of deficiencies of individual gene subset is that its contribution to classification purpose is limited. This issue can be alleviated by ensemble gene selection based on random selection to some extend. However, the random one requires an unnecessary large number of candidate gene subsets and its reliability is a problem. In this study, we propose a new ensemble method, called ensemble gene selection by grouping (EGSG), to select multiple gene subsets for the classification purpose. Rather than selecting randomly, our method chooses salient gene subsets from microarray data by virtue of information theory and approximate Markov blanket. The effectiveness and accuracy of our method is validated by experiments on five publicly available microarray data sets. The experimental results show that our ensemble gene selection method has comparable classification performance to other gene selection methods, and is more stable than the random one.