Feature selection by markov chain Monte Carlo sampling - A bayesian approach

Abstract

We redefine the problem of feature selection as one of model selection and propose to use a Markov Chain Monte Carlo method to sample models. The applicability of our method is related to Bayesian network classifiers. Simulation experiments indicate that our novel proposal distribution results in an ignorant proposal prior. Finally, it is shown how the sampling can be controlled by a regularization prior. ©Springer-Verlag 004.