A Liouville-based approach for discrete data categorization

Authors:
Nizar Bouguila
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada, Qc
Venue:
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Year:
2011

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Abstract

In this paper, we describe a learning approach based on the smoothing of multinomial estimates using Beta-Liouville distributions. Like the Dirichlet, the Beta-Liouville is conjugate to the multinomial. It has, however, an important advantage which is its more general covariance matrix. Empirical results indicate that the proposed approach outperforms previous smoothing techniques based mainly on the Dirichlet distribution.