Active Learning for Causal Bayesian Network Structure with Non-symmetrical Entropy
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2008-06-26
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Abstract
Causal knowledge is crucial for facilitating comprehension, diagnosis, prediction, and control in automated reasoning. Active learning in Bayesian networks involves interventions by manipulating specific variables or their interactions, and observing the patterns of change over the other variables to derive causal relationships for knowledge discovery. 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 information entropy and a stop criterion based on minimizing structure entropy of the resulting networks. We examine the technical challenges and practical issues in developing effective node selection and stopping criteria in our method. Experimental results on a set of benchmark Bayesian networks are promising. The proposed method is applicable in many real-life applications where multiple instances are simultaneously sampled as a data set in each active learning step.