Most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, variables are selected sequentially, on
a one-by-one basis, based upon expected information gain. While myopic test selection is not realistic for many medical applications, non-myopic test selection,
in which information gain would be computed for all combinations of variables,
would be too demanding. We present three new test-selection algorithms for probabilistic networks, which all employ knowledge-based clusterings of variables;
these are a myopic algorithm, a non-myopic algorithm and a semi-myopic algorithm. In a preliminary evaluation, the semi-myopic algorithm proved to generate
a satisfactory test strategy, with little computational burden.
|Name||CTIT Technical Report Series|
|Publisher||Centre for Telematics and Information Technology, University of Twente|