Feature selection using counting grids: application to microarray data

Authors:
Pietro Lovato;Manuele Bicego;Marco Cristani;Nebojsa Jojic;Alessandro Perina
Affiliations:
Computer Science Department, University of Verona, Italy;Computer Science Department, University of Verona, Italy;Computer Science Department, University of Verona, Italy;Microsoft Research;Microsoft Research
Venue:
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2012

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Abstract

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.