Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2009
Abstract
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.
Keywords
Active learning, Bayesian networks, Intervention, Node selection, Non-symmetrical entropy, Stop criterion
Discipline
Databases and Information Systems
Publication
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
First Page
290
Last Page
301
ISBN
9783642013065
Identifier
10.1007/978-3-642-01307-2_28
Publisher
Springer-Verlag
City or Country
Bangkok,Thailand
Citation
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.
Available at: https://ink.library.smu.edu.sg/sis_research/2983
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