Conference Proceeding Article
Causal knowledge is crucial for facilitating comprehension, diagnosis, prediction, and control in automated reasoning. Active learning in causal Bayesian networks involves interventions by manipulating specific variables, and observing the patterns of change over other variables to derive causal knowledge. In this paper, we propose a new active learning approach that supports interventions with node selection. Our method admits a node selection criterion based on non-symmetrical entropy from the current data and a stop criterion based on structure entropy of the resulting networks. We examine the technical challenges and practical issues involved. Experimental results on a set of benchmark Bayesian networks are promising. The proposed method is potentially useful in many real-life applications where multiple instances are collected as a data set in each active learning step. © Springer-Verlag Berlin Heidelberg 2009.
Active learning, Bayesian networks, Intervention, Node selection, Non-symmetrical entropy, Stop criterion
Databases and Information Systems
Intelligent Systems and Decision Analytics
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
City or Country
Li G. and Tze-Yun LEONG.
Active learning for causal bayesian network structure with non-symmetrical entropy. (2009). PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 290-301. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2983