Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Uncertainty and vagueness in knowledge based systems
Uncertainty and vagueness in knowledge based systems
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Probabilistic similarity networks
Probabilistic similarity networks
C4.5: programs for machine learning
C4.5: programs for machine learning
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Model complexity vs. performance in the bayesian optimization algorithm
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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The K2 metric is a well-known evaluation measure (or scoring function) for learning Bayesian networks from data [7]. It is derived by assuming uniform prior distributions on the values of an attribute for each possible instantiation of its parent attributes. This assumption introduces a tendency to select simpler network structures. In this paper we modify the K2 metric in three different ways, introducing a parameter by which the strength of this tendency can be controlled. Our experiments with the ALARM network [2] and the BOBLO network [17] suggest that--somewhat contrary to our expectations--a slightly stronger tendency towards simpler structures may lead to even better results.