Paper Info
Title
A simple bayesian algorithm for feature ranking in high dimensional regression problems
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.
Year
DOI
Venue
2011
10.1007/978-3-642-25832-9_23
Australasian Conference on Artificial Intelligence
Keywords
Field
DocType
fundamental importance,novel bayesian algorithm,empirical comparison,potential predictor feature,feature ranking,hundred sample,artificial experiment,classification problem,bfr algorithm,marginal covariate screening,simple bayesian algorithm,high dimensional regression problem
Data set,Covariate,Regression,Feature selection,Feature ranking,Parametric statistics,Artificial intelligence,Random forest,Credible interval,Machine learning,Mathematics
Conference
Volume
ISSN
Citations 
7106
0302-9743
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Enes Makalic15511.54
Daniel F. Schmidt25110.68