A Simple Bayesian Algorithm for Feature Ranking in High Dimensional Regression Problems
作者： Enes Makalic , Daniel F. Schmidt
DOI: 10.1007/978-3-642-25832-9_23
关键词: Simple (abstract algebra) 、 Mathematics 、 Machine learning 、 Random forest 、 Feature ranking 、 Artificial intelligence 、 Regression 、 Parametric statistics 、 Credible interval 、 Covariate 、 Feature selection
摘要: Variable selection or feature ranking is a problem of fundamental importance in modern scientific research where data sets comprising hundreds thousands potential predictor features and only few hundred samples are not uncommon. This paper introduces novel Bayesian algorithm for (BFR) which does require any user specified parameters. The BFR very general can be applied to both parametric regression classification problems. An empirical comparison against random forests marginal covariate screening demonstrates promising performance real artificial experiments.
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