Identifying markov blankets using lasso estimation

Li, Gang, Dai, Honghua and Tu, Yiqing 2004, Identifying markov blankets using lasso estimation, Lecture notes in computer science, vol. 3056, no. 2004, pp. 308-318.

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Title Identifying markov blankets using lasso estimation
Author(s) Li, GangORCID iD for Li, Gang
Dai, HonghuaORCID iD for Dai, Honghua
Tu, Yiqing
Journal name Lecture notes in computer science
Volume number 3056
Issue number 2004
Start page 308
End page 318
Publisher Springer Berlin
Place of publication Berlin, Germany
Publication date 2004-04-22
ISSN 0302-9743
Summary Determining the causal relation among attributes in a domain is a key task in data mining and knowledge discovery. The Minimum Message Length (MML) principle has demonstrated its ability in discovering linear causal models from training data. To explore the ways to improve efficiency, this paper proposes a novel Markov Blanket identification algorithm based on the Lasso estimator. For each variable, this algorithm first generates a Lasso tree, which represents a pruned candidate set of possible feature sets. The Minimum Message Length principle is then employed to evaluate all those candidate feature sets, and the feature set with minimum message length is chosen as the Markov Blanket. Our experiment results show the ability of this algorithm. In addition, this algorithm can be used to prune the search space of causal discovery, and further reduce the computational cost of those score-based causal discovery algorithms.

Language eng
Field of Research 080299 Computation Theory and Mathematics not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2004 Springer-Verlag Berlin Heidelberg
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Document type: Journal Article
Collection: School of Information Technology
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