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.
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© 2007 Springer-Verlag Berlin Heidelberg
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Sent, D., van der Gaag, L.C. (2007). Enhancing Automated Test Selection in Probabilistic Networks. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_44
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DOI: https://doi.org/10.1007/978-3-540-73599-1_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
Online ISBN: 978-3-540-73599-1
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