Exploiting association and correlation rules parameters for improving the K2 algorithm
Year: 2004
Type: conference-paper
Abstract: A Bayesian network is an appropriate tool to deal with the uncertainty that is typical of real-life applications. Bayesian network arcs represent statistical dependence between different variables. In the data mining field, association and correlation rules can be interpreted as well as expressing statistical dependence relations. K2 is a well-known algorithm which is able to learn Bayesian network. \n In this paper we present two extensions of K2 called K2-Lift and K2-X2 that \n exploit two parameters normally defined in relation to association and correlation rules for \n learning Bayesian networks. The experiments performed show that K2-Lift and K2-X2 improve K2 \n with respect to both the quality of the learned network and the execution time
Institutions: University of Ferrara, University of Bologna
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W53345041