Machine Learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Feature selection for classifying high-dimensional numerical data
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Virtual gene: using correlations between genes to select informative genes on microarray datasets
Transactions on Computational Systems Biology II
BISAR: boosted input selection algorithm for regression
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Gene selection is usually the crucial first step in microarray data analysis. One class of typical approaches is to calculate some discriminative scores using data associated with a single gene. Such discriminative scores are then sorted and top ranked genes are selected for further analysis. However, such an approach will result in redundant gene set since it ignores the complex relationships between genes. Recent researches in feature subset selection began to tackle this problem by limiting the correlations of the selected feature set. In this paper, we propose a novel general framework BFSS: Boost Feature Subset Selection to improve the performance of single-gene based discriminative scores using bootstrapping techniques. Features are selected from dynamically adjusted bootstraps of the training dataset. We tested our algorithm on three well-known publicly available microarray data sets in the bioinformatics community. Encouraging results are reported in this paper.