Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks
ECML '02 Proceedings of the 13th European Conference on Machine Learning
An Improved Approach for the Discovery of Causal Models via MML
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Boosting Density Function Estimators
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Linear Causal Model Discovery Using the MML criterion
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A study of causal discovery with weak links and small samples
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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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.