Please use this identifier to cite or link to this item:
|Title:||Active learning for causal bayesian network structure with non-symmetrical entropy||Authors:||Li, G.
|Issue Date:||2009||Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Apr 15, 2021
checked on Apr 14, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.