Study of ensemble strategies in discovering linear causal models
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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Determining the causal structure of a domain is a keytask in the area of Data Mining and Knowledge Discovery.The algorithm proposed by Wallace et al. [15] hasdemonstrated its strong ability in discovering Linear CausalModels from given data sets. However, some experimentsshowed that this algorithm experienced difficulty in discoveringlinear relations with small deviation, and it occasion-allygives a negative message length, which should not beallowed. In this paper, a more efficient and precise MML encodingscheme is proposed to describe the model structureand the nodes in a Linear Causal Model. The estimation ofdifferent parameters is also derived. Empirical results showthat the new algorithm outperformed the previous MML-basedalgorithm in terms of both speed and precision.