Since most algorithms for Bayesian Belief Network (BBN) creation require
discrete data, a method to convert continuous data into discrete is used
in a pre-processing step. An information-theoretic metric is used in a
recently published article by myself and Bruce Barton. The discretization
method can be extended to dynamically combine existing partitions when
creating a BBN, if a Minimum Descriptive Length (MDL) metric is used to
guide the BBN creation.
This method does not rely on assumptions about the data distribution and
is intentionally designed to handle 'multi-modal' data distributions with
a minimal loss of information.
The reference is:
Clarke,E., and Barton,B., (2000), Entropy and MDL Discretization of
Continuous Variables for Bayesian Belief Networks. International Journal
of Intelligent Systems, 15, 61-92.
Another relatively recent article on the same topic is:
Monti,S., and Cooper,G., (1998), A Multivariate Method for Learning
Bayesian Networks from Mixed Data, Proc. Uncertainty in Artificial
Intelligence, ed. Cooper,G., and Moral,S., Morgan Kaufmann, S.F.
I hope this helps.
Ellis
On Thu, 6 Apr 2000, Zhu wrote:
> Hello all,
>
> Is there any algorithms of Bayesian Network to work directly on the
> mixture of continuous and categorical variables?
>
> The classification problem that I am working on has 37 input variables, 15
> of them are categorical and the rest of them are continuous. To my
> understanding, I need to discretize the continuous varibles in order to
> apply some commonly used algorithms (such as junction tree) to construct
> and estimate BNs. Since a large portion of the input variables are
> continuous, I am afraid of loss of information by discretizing them.
> References and input on working directly on the mixture will
> be highly appreciated. I would also like to have any comments and
> experiences on how much gain we can get from working on the mixture
> directly over transforming all variables into discrete. Thanks.
>
>
> Best regards,
>
> Julie
>
>
_____________________________________________________________________
Ellis Clarke, Ph.D.; CSEE, University of Maryland Baltimore County;
clarke@cs.umbc.edu
_____________________________________________________________________
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