An improved approach for the discovery of causal models via MML

Dai, Honghua and Li, Gang 2002, An improved approach for the discovery of causal models via MML, in Advances in knowledge discovery and data mining : 6th Pacific-Asia conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002 : proceedings, Springer Berlin, Berlin, Germany, pp. 304-315.

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Title An improved approach for the discovery of causal models via MML
Author(s) Dai, Honghua
Li, Gang
Conference name Pacific-Asia Conference on Knowledge Discovery and Data Mining (6th : 2002 : Taipei, Taiwan)
Conference location Taipei, Taiwan
Conference dates 6-8 May 2002
Title of proceedings Advances in knowledge discovery and data mining : 6th Pacific-Asia conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002 : proceedings
Editor(s) Chen, Ming-Syan
Yu, Philip S.
Liu, Bing
Publication date 2002
Series Lecture notes in computer science ; 2336.
Start page 304
End page 315
Publisher Springer Berlin
Place of publication Berlin, Germany
Keyword(s) minimum message length
MML
causal discovery
causal modeling
inductive inference
machine learning
Bayesian networks
Summary Discovering a precise causal structure accurately reflecting the given data is one of the most essential tasks in the area of data mining and machine learning. One of the successful causal discovery approaches is the information-theoretic approach using the Minimum Message Length Principle[19]. This paper presents an improved and further experimental results of the MML discovery algorithm. We introduced a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. The experimental results of the current version of the discovery system show that: (1) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal models with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity.
Notes SpringerLink Date Tuesday, January 01, 2002
ISBN 3540437045
9783540437048
ISSN 0302-9743
1611-3349
Language eng
Field of Research 080105 Expert Systems
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2002 Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004876

Document type: Conference Paper
Collection: School of Information Technology
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