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of features are rather easy to acquire in many applications, and therefore can provide
a promising way to cope with high dimensionality.
Grouped feature selection based on regularized regression provides an intuitive
way to incorporate grouping information of features into feature selection, in a sta-
tistically sound and computationally efficient way. The regularizers in these methods
are also flexible to change, making it possible to adapt for future applications that
would come with different structures of groups such as hierarchies.
A case study of feature selection on exon microarray data has illustrated that
grouped feature selection would provide not only better prediction performance, but
also potentially new understanding of complex biological systems. Consistency is
still an open problem to solve when sample sizes are much smaller than the dimen-
sionality in data, especially for biomedical applications of grouped feature selection
methods.
Acknowledgments This work has been supported by Deutsche Forschungsgemeinschaft (DFG)
within theCollaborativeResearchCenter SFB876 “Providing Information byResource-Constrained
Analysis”, project C1.
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