Study of ensemble strategies in discovering linear casual models

Li, Gang and Dai, Honghua 2005, Study of ensemble strategies in discovering linear casual models, Lecture notes in computer science, vol. 3614/2005, pp. 368-377.

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Title Study of ensemble strategies in discovering linear casual models
Author(s) Li, GangORCID iD for Li, Gang
Dai, HonghuaORCID iD for Dai, Honghua
Journal name Lecture notes in computer science
Volume number 3614/2005
Start page 368
End page 377
Publisher Springer-Verlag
Place of publication Berlin, Germany
Publication date 2005
ISSN 0302-9743
Keyword(s) algorithms
distributed computer systems
knowledge based systems
data mining
ensemble strategies
Summary 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.
Language eng
Field of Research 080299 Computation Theory and Mathematics not elsewhere classified
Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2005, Springer-Verlag Berlin Heidelberg
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Collection: School of Information Technology
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