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An improved approach for the discovery of causal models via MML
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.
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Title of proceedings
Advances in knowledge discovery and data mining : 6th Pacific-Asia conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002 : proceedingsEvent
Pacific-Asia Conference on Knowledge Discovery and Data Mining (6th : 2002 : Taipei, Taiwan)Series
Lecture notes in computer science ; 2336.Pagination
304 - 315Publisher
Springer BerlinLocation
Taipei, TaiwanPlace of publication
Berlin, GermanyPublisher DOI
Start date
2002-05-06End date
2002-05-08ISSN
0302-9743eISSN
1611-3349ISBN-13
9783540437048ISBN-10
3540437045Language
engNotes
SpringerLink Date Tuesday, January 01, 2002Publication classification
E1 Full written paper - refereedCopyright notice
2002 Springer-Verlag Berlin HeidelbergEditor/Contributor(s)
M Chen, P Yu, B LiuUsage metrics
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