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Discretizing Continuous Attributes While Learning Bayesian Networks (1996)
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| Venue: | In Proc. ICML |
| Citations: | 78 - 4 self |
BibTeX
@INPROCEEDINGS{Friedman96discretizingcontinuous,
author = {Nir Friedman and Moises Goldszmidt},
title = {Discretizing Continuous Attributes While Learning Bayesian Networks},
booktitle = {In Proc. ICML},
year = {1996},
pages = {157--165},
publisher = {Morgan Kaufmann}
}
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Abstract
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in this method is a new metric based on the Minimal Description Length principle for choosing the threshold values for the discretization while learning the Bayesian network structure. This score balances the complexity of the learned discretization and the learned network structure against how well they model the training data. This ensures that the discretization of each variable introduces just enough intervals to capture its interaction with adjacent variables in the network. We formally derive the new metric, study its main properties, and propose an iterative algorithm for learning a discretization policy. Finally, we illustrate its behavior in applications to supervised learning. 1 INTRODUCTION Bayesian networks provide efficient and effective representation of the joint probability distribution over a set ...
Keyphrases
bayesian network continuous attribute learning process bayesian network structure adjacent variable effective representation discretization policy introduction bayesian network learned discretization threshold value minimal description length principle joint probability distribution training data iterative algorithm integral part main ingredient main property learned network structure continuous variable variable introduces
