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On the Behaviour of Information Measures for Test Selection

Centre for Telematics and Information Technology, University of Twente
Publication Date
  • Computer Science
  • Medicine


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.

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