In this paper a novel feature selection scheme is proposed, which exploits the potentialities of a recent probabilistic generative model, the Counting Grid. This model is able to cluster together similar observations, highlighting the compactness of a class and its underlying structure. The proposed feature selection scheme is applied to the expression microarray scenario, a peculiar context with very few patterns and a huge number of features. Experiments on benchmark datasets show that the proposed approach is effective and stable, assessing state-of-the-art classification accuracies. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Lovato, P., Bicego, M., Cristani, M., Jojic, N., & Perina, A. (2012). Feature selection using counting grids: Application to microarray data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7626 LNCS, pp. 629–637). https://doi.org/10.1007/978-3-642-34166-3_69
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