Elements of information theory
Elements of information theory
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Non-parametric classifier-independent feature selection
Pattern Recognition
Markov blanket-embedded genetic algorithm for gene selection
Pattern Recognition
Classifier ensembles: Select real-world applications
Information Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Ensemble methods for classification of patients for personalized medicine with high-dimensional data
Artificial Intelligence in Medicine
Selecting differentially expressed genes using minimum probability of classification error
Journal of Biomedical Informatics
Bioinformatics
Artificial Intelligence in Medicine
A review of feature selection techniques in bioinformatics
Bioinformatics
Monte Carlo feature selection for supervised classification
Bioinformatics
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Analysis & Applications - Special Issue: Non-parametric distance-based classification techniques and their applications
APPLYING DATA MINING TECHNIQUES FOR CANCER CLASSIFICATION ON GENE EXPRESSION DATA
Cybernetics and Systems
Artificial Intelligence in Medicine
Feature selection with dynamic mutual information
Pattern Recognition
Artificial Intelligence in Medicine
Learning to classify by ongoing feature selection
Image and Vision Computing
An Evolutionary Algorithm Approach to Optimal Ensemble Classifiers for DNA Microarray Data Analysis
IEEE Transactions on Evolutionary Computation
Feature selection using hierarchical feature clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Noisy data elimination using mutual k-nearest neighbor for classification mining
Journal of Systems and Software
InstanceRank based on borders for instance selection
Pattern Recognition
Algorithms for discovery of multiple Markov boundaries
The Journal of Machine Learning Research
Gene selection with guided regularized random forest
Pattern Recognition
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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Cancer diagnosis is an important emerging clinical application of microarray data. Its accurate prediction to the type or size of tumors relies on adopting powerful and reliable classification models, so as to patients can be provided with better treatment or response to therapy. However, the high dimensionality of microarray data may bring some disadvantages, such as over-fitting, poor performance and low efficiency, to traditional classification models. Thus, one of the challenging tasks in cancer diagnosis is how to identify salient expression genes from thousands of genes in microarray data that can directly contribute to the phenotype or symptom of disease. In this paper, we propose a new ensemble gene selection method (EGS) to choose multiple gene subsets for classification purpose, where the significant degree of gene is measured by conditional mutual information or its normalized form. After different gene subsets have been obtained by setting different starting points of the search procedure, they will be used to train multiple base classifiers and then aggregated into a consensus classifier by the manner of majority voting. The proposed method is compared with five popular gene selection methods on six public microarray datasets and the comparison results show that our method works well.