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Study of ensemble strategies in discovering linear causal 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. © Springer-Verlag Berlin Heidelberg 2005.
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Volume
3614Issue
PART IIPagination
368 - 377Publisher DOI
ISSN
0302-9743Publication classification
E1.1 Full written paper - refereedTitle of proceedings
Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)Usage metrics
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