A comparison on score spaces for expression microarray data classification

Authors:
Alessandro Perina;Pietro Lovato;Marco Cristani;Manuele Bicego
Affiliations:
Microsoft Research, Redmond;University of Verona, Department of Computer Science, Verona, Italy;University of Verona, Department of Computer Science, Verona, Italy and Italian Institute of Technology, Genoa, Italy;University of Verona, Department of Computer Science, Verona, Italy
Venue:
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Year:
2011

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Abstract

In this paper an empirical evaluation of different generative scores for expression microarray data classification is proposed. Score spaces represent a quite recent trend in the machine learning community, taking the best of both generative and discriminative classification paradigms. The scores are extracted from topic models, a class of highly interpretable probabilistic tools whose utility in the microarray classification context has been recently assessed. The experimental evaluation, performed on 3 literature datasets and with 7 score spaces, demonstrates the viability of the proposed scheme and, for the first time, it compares pros and cons of each space.