Enhancing Automated Test Selection in Probabilistic Networks

Abstract. Most test-selection algorithms currently in use with probabilistic networks select variables myopically, that is, test 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 study, the semi-myopic algorithm proved to generate a satisfactory test strategy, with little computational burden.

• Type: Conference
• Year: 2007
• Where: AIME
• Authors: Danielle Sent, Linda C. van der Gaag
• Keywords: AIME 2007, Artificial Intelligence, Myopic Test Selection, Networks Select Variables, Probabilistic Networks