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An empirical investigation of the K2 metric

By Christian Borgelt and Rudolf Kruse


Abstract. 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.

Publisher: Springer
Year: 2010
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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