Ensembling MML causal discovery

Dai, Honghua, Li, Gang and Zhou, Zhi-Hua 2004, Ensembling MML causal discovery, Lecture notes in computer science, vol. 3056, pp. 260-271.

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Title Ensembling MML causal discovery
Author(s) Dai, Honghua
Li, Gang
Zhou, Zhi-Hua
Journal name Lecture notes in computer science
Volume number 3056
Start page 260
End page 271
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2004
ISSN 0302-9743
1611-3349
Summary This paper presents an ensemble MML approach for the discovery of causal models. The component learners are formed based on the MML causal induction methods. Six different ensemble causal induction algorithms are proposed. Our experiential results reveal that (1) the ensemble MML causal induction approach has achieved an improved result compared with any single learner in terms of learning accuracy and correctness; (2) Among all the ensemble causal induction algorithms examined, the weighted voting without seeding algorithm outperforms all the rest; (3) It seems that the ensembled CI algorithms could alleviate the local minimum problem. The only drawback of this method is that the time complexity is increased by δ times, where δ is the ensemble size.
Notes Book Title: Advances in knowledge discovery and data mining
Language eng
Field of Research 080299 Computation Theory and Mathematics not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30002399

Document type: Journal Article
Collection: School of Information Technology
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