Active learning for causal bayesian network structure with non-symmetrical entropy

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 kno...

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Main Authors:	Li G., Tze-Yun LEONG
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2009
Subjects:	Active learning Bayesian networks Intervention Node selection Non-symmetrical entropy Stop criterion Databases and Information Systems
Online Access:	https://ink.library.smu.edu.sg/sis_research/2983 https://ink.library.smu.edu.sg/context/sis_research/article/3983/viewcontent/PAKDD09.pdf
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Institution:	Singapore Management University
Language:	English

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spelling	sg-smu-ink.sis_research-39832018-07-13T04:33:48Z Active learning for causal bayesian network structure with non-symmetrical entropy Li G., Tze-Yun LEONG, 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. 2009-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2983 info:doi/10.1007/978-3-642-01307-2_28 https://ink.library.smu.edu.sg/context/sis_research/article/3983/viewcontent/PAKDD09.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Active learning Bayesian networks Intervention Node selection Non-symmetrical entropy Stop criterion Databases and Information Systems
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Active learning Bayesian networks Intervention Node selection Non-symmetrical entropy Stop criterion Databases and Information Systems
spellingShingle	Active learning Bayesian networks Intervention Node selection Non-symmetrical entropy Stop criterion Databases and Information Systems Li G., Tze-Yun LEONG, Active learning for causal bayesian network structure with non-symmetrical entropy
description	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.
format	text
author	Li G., Tze-Yun LEONG,
author_facet	Li G., Tze-Yun LEONG,
author_sort	Li G.,
title	Active learning for causal bayesian network structure with non-symmetrical entropy
title_short	Active learning for causal bayesian network structure with non-symmetrical entropy
title_full	Active learning for causal bayesian network structure with non-symmetrical entropy
title_fullStr	Active learning for causal bayesian network structure with non-symmetrical entropy
title_full_unstemmed	Active learning for causal bayesian network structure with non-symmetrical entropy
title_sort	active learning for causal bayesian network structure with non-symmetrical entropy
publisher	Institutional Knowledge at Singapore Management University
publishDate	2009
url	https://ink.library.smu.edu.sg/sis_research/2983 https://ink.library.smu.edu.sg/context/sis_research/article/3983/viewcontent/PAKDD09.pdf
_version_	1770572766345428992

Active learning for causal bayesian network structure with non-symmetrical entropy

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