Article reference:

M. Egmont-Petersen. "Feature selection by Markov Chain Monte Carlo Sampling - a Bayesian approach, " In Structural, Syntactic, and Statistical Pattern Recognition, Proceedings of the Joint IAPR Workshops SSPR 2004 and SPR 2004, Lecture Notes in Computer Science 3138, Eds. A. Fred et al., pp. 1034-1042, 2004.


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.

Electronic reprint , or contact me:  michael * (with * indicating @)