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Ensembling MML causal discovery
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.
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Journal
Lecture notes in computer scienceVolume
3056Pagination
260 - 271Publisher
Springer-VerlagLocation
Berlin, GermanyISSN
0302-9743eISSN
1611-3349Language
engNotes
Book Title: Advances in knowledge discovery and data miningPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2004, Springer-Verlag Berlin HeidelbergUsage metrics
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