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- W2134144489 abstract "We focus on a well-known classification task with expert systems based on Bayesian networks : predicting the state of a target variable given an incomplete observation of the other variables in the network, i.e., an observation of a subset of all the possible variables. To provide conclusions robust to near-ignorance about the process that prevents some of the variables from being observed, it has recently been derived a new rule, called conservative updating . With this paper we address the problem to efficiently compute the conservative updating rule for robust classification with Bayesian networks. We show first that the general problem is NP-hard , thus establishing a fundamental limit to the possibility to do robust classification efficiently. Then we define a wide subclass of Bayesian networks that does admit efficient computation. We show this by developing a new classification algorithm for such a class, which extends substantially the limits of efficient computation with respect to the previously existing algorithm. The algorithm is formulated as a variable elimination procedure, whose computation time is linear in the input size." @default.
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- W2134144489 date "2007-03-01" @default.
- W2134144489 modified "2023-09-24" @default.
- W2134144489 title "Fast algorithms for robust classification with Bayesian nets" @default.
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- W2134144489 doi "https://doi.org/10.1016/j.ijar.2006.07.011" @default.
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