A simple bayesian algorithm for feature ranking in high dimensional regression problems

Authors:
Enes Makalic;Daniel F. Schmidt
Affiliations:
Centre for MEGA Epidemiology, The University of Melbourne, Carlton, VIC, Australia;Centre for MEGA Epidemiology, The University of Melbourne, Carlton, VIC, Australia
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

Citing 3
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Abstract

Variable selection or feature ranking is a problem of fundamental importance in modern scientific research where data sets comprising hundreds of thousands of potential predictor features and only a few hundred samples are not uncommon. This paper introduces a novel Bayesian algorithm for feature ranking (BFR) which does not require any user specified parameters. The BFR algorithm is very general and can be applied to both parametric regression and classification problems. An empirical comparison of BFR against random forests and marginal covariate screening demonstrates promising performance in both real and artificial experiments.