Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-01307-2_28
Title: Active learning for causal bayesian network structure with non-symmetrical entropy
Authors: Li, G. 
Leong, T.-Y. 
Keywords: Active learning
Bayesian networks
Intervention
Node selection
Non-symmetrical entropy
Stop criterion
Issue Date: 2009
Source: Li, G.,Leong, T.-Y. (2009). Active learning for causal bayesian network structure with non-symmetrical entropy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5476 LNAI : 290-301. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-01307-2_28
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.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41078
ISBN: 3642013066
ISSN: 03029743
DOI: 10.1007/978-3-642-01307-2_28
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