Skip to main content
Article thumbnail
Location of Repository

An empirical investigation of the K2 metric

By Christian Borgelt and Rudolf Kruse

Abstract

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:10.1.1.162.2092
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://fuzzy.cs.uni-magdeburg.... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.