Informative Priors for Markov Blanket Discovery
Abstract
We present a novel interpretation of information theoretic feature selection as optimization of a discriminative model. We show that this formulation coincides with a group of mutual information based filter heuristics in the literature, and show how our probabilistic framework gives a well-founded extension for informative priors. We then derive a particular sparsity prior that recovers the well-known IAMB algorithm (Tsamardinos & Aliferis, 2003) and extend it to create a novel algorithm, IAMB-IP, that includes domain knowledge priors. In empirical evaluations, we find the new algorithm to improve Markov Blanket recovery even when a misspecified prior was used, in which half the prior knowledge was incorrect.
Cite
Text
Pocock et al. "Informative Priors for Markov Blanket Discovery." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.Markdown
[Pocock et al. "Informative Priors for Markov Blanket Discovery." Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.](https://mlanthology.org/aistats/2012/pocock2012aistats-informative/)BibTeX
@inproceedings{pocock2012aistats-informative,
title = {{Informative Priors for Markov Blanket Discovery}},
author = {Pocock, Adam and Lujan, Mikel and Brown, Gavin},
booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics},
year = {2012},
pages = {905-913},
volume = {22},
url = {https://mlanthology.org/aistats/2012/pocock2012aistats-informative/}
}