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Abstract: 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... (Update)
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BibTeX entry: (Update)
@misc{ gang-ensembling,
author = "H. Dai, G. Li, Z.-H. Zhou"
title = "Ensembling MML Causal Discovery"
booktitle = "Lecture Notes in Artificial Intelligence 3056, 2004, pp.260-271"
address = "Berlin: Springer"
page = "260-271"
year = "2004",
url = "citeseer.ist.psu.edu/article/dai04ensembling.html" }
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