Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Mining top-K covering rule groups for gene expression data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Significance of Gene Ranking for Classification of Microarray Samples
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Selecting features in microarray classification using ROC curves
Pattern Recognition
A review of feature selection techniques in bioinformatics
Bioinformatics
RDCurve: A Nonparametric Method to Evaluate the Stability of Ranking Procedures
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A solution to the curse of dimensionality problem in pairwise scoring techniques
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
A local information-based feature-selection algorithm for data regression
Pattern Recognition
A feature selection method using improved regularized linear discriminant analysis
Machine Vision and Applications
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Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides genes into subsets, the sizes of which are relatively small (roughly of size h), then selects informative smaller subsets of genes (of size r