Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Random subspace method for multivariate feature selection
Pattern Recognition Letters
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
A review of feature selection techniques in bioinformatics
Bioinformatics
APPLYING DATA MINING TECHNIQUES FOR CANCER CLASSIFICATION ON GENE EXPRESSION DATA
Cybernetics and Systems
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
Independent component analysis algorithms for microarray data analysis
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
A novel hybrid feature selection method for microarray data analysis
Applied Soft Computing
International Journal of Approximate Reasoning
Evolutionary Rough Feature Selection in Gene Expression Data
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Gene expression microarray data provides simultaneous activity measurement of thousands of features facilitating a potential effective and reliable cancer diagnosis. An important and challenging task in microarray analysis refers to selecting the most relevant and significant genes for data cancer classification. A random subspace ensemble based method is proposed to address feature selection in gene expression cancer diagnosis. The introduced Diverse Accurate Feature Selection method relies on multiple individual classifiers built based on random feature subspaces. Each feature is assigned a score computed based on the pairwise diversity among individual classifiers and the ratio between individual and ensemble accuracies. This triggers the creation of a ranked list of features for which a final classifier is applied with an increased performance using minimum possible number of genes. Experimental results focus on the problem of gene expression cancer diagnosis based on microarray datasets publicly available. Numerical results show that the proposed method is competitive with related models from literature.