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Study of ensemble strategies in discovering linear casual models
Determining the causal structure of a domain is frequently a key task in the area of Data Mining and Knowledge Discovery. This paper introduces ensemble learning into linear causal model discovery, then examines several algorithms based on different ensemble strategies including Bagging, Adaboost and GASEN. Experimental results show that (1) Ensemble discovery algorithm can achieve an improved result compared with individual causal discovery algorithm in terms of accuracy; (2) Among all examined ensemble discovery algorithms, BWV algorithm which uses a simple Bagging strategy works excellently compared to other more sophisticated ensemble strategies; (3) Ensemble method can also improve the stability of parameter estimation. In addition, Ensemble discovery algorithm is amenable to parallel and distributed processing, which is important for data mining in large data sets.
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Journal
Lecture notes in computer scienceVolume
3614/2005Pagination
368 - 377Publisher
Springer-VerlagLocation
Berlin, GermanyISSN
0302-9743eISSN
1611-3349Language
engPublication classification
C1 Refereed article in a scholarly journal; C Journal articleCopyright notice
2005, Springer-Verlag Berlin HeidelbergUsage metrics
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