Abstract
For a presented case, a Bayesian network classifier in essence computes a posterior probability distribution over its class variable. Based upon this distribution, the classifier’s classification function returns a single, determinate class value and thereby hides the uncertainty involved. To provide reliable decision support, however, the classifier should be able to convey indecisiveness if the posterior distribution computed for the case does not clearly favour one class value over another. In this paper we present an approach for this purpose, and introduce new measures to capture the performance and practicability of such classifiers.
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van der Gaag, L.C., Renooij, S., Steeneveld, W., Hogeveen, H. (2009). When in Doubt ... Be Indecisive. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_45
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DOI: https://doi.org/10.1007/978-3-642-02906-6_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02905-9
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