Enhancing Automated Test Selection in Probabilistic Networks
نویسندگان
چکیده
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. keywords: diagnostic test selection, probabilistic network, semi-myopia
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