An Empirical Investigation of the K2 Metric

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.

• Type: Conference
• Year: 2001
• Where: ECSQARU
• Authors: Christian Borgelt, Rudolf Kruse
• Keywords: Automated Reasoning, ECSQARU 2001, K2 Metric, Simpler Network Structures, Well-known Evaluation Measure