Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
A review of feature selection techniques in bioinformatics
Bioinformatics
Criteria Ensembles in Feature Selection
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Gene ranking and biomarker discovery under correlation
Bioinformatics
Improving stability of feature selection methods
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variance Reduction Framework for Stable Feature Selection
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Robust variable selection through MAVE
Computational Statistics & Data Analysis
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Feature selection is an important step when building a classifier on high dimensional data. As the number of observations is small, the feature selection tends to be unstable. It is common that two feature subsets, obtained from different datasets but dealing with the same classification problem, do not overlap significantly. Although it is a crucial problem, few works have been done on the selection stability. The behavior of feature selection is analyzed in various conditions, not exclusively but with a focus on t-score based feature selection approaches and small sample data. The analysis is in three steps: the first one is theoretical using a simple mathematical model; the second one is empirical and based on artificial data; and the last one is based on real data. These three analyses lead to the same results and give a better understanding of the feature selection problem in high dimension data.