NDLI: Explaining inferences in Bayesian networks

Content Provider	Springer Nature Link
Author	Yap, Ghim Eng Tan, Ah Hwee Pang, Hwee Hwa
Copyright Year	2007
Abstract	While Bayesian network (BN) can achieve accurate predictions even with erroneous or incomplete evidence, explaining the inferences remains a challenge. Existing approaches fall short because they do not exploit variable interactions and cannot account for compensations during inferences. This paper proposes the Explaining BN Inferences (EBI) procedure for explaining how variables interact to reach conclusions. EBI explains the value of a target node in terms of the influential nodes in the target’s Markov blanket under specific contexts, where the Markov nodes include the target’s parents, children, and the children’s other parents. Working back from the target node, EBI shows the derivation of each intermediate variable, and finally explains how missing and erroneous evidence values are compensated. We validated EBI on a variety of problem domains, including mushroom classification, water purification and web page recommendation. The experiments show that EBI generates high quality, concise and comprehensible explanations for BN inferences, in particular the underlying compensation mechanism that enables BN to outperform alternative prediction systems, such as decision tree.
Starting Page	263
Ending Page	278
Page Count	16
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	29
Issue Number	3
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2007-10-07
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bayesian networks Explanations Inferences Compensations Error values Missing values Manufacturing, Machines, Tools Mechanical Engineering Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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