An empirical study of encoding schemes and search strategies in discovering causal networks

Dai, H., Li, Gang and Tu, Yiqing 2002, An empirical study of encoding schemes and search strategies in discovering causal networks, in Machine Learning: ECML 2002: Proceedings of the 13th European Conference on Machine Learning, Springer Berlin, Berlin, Germany, pp. 48-59.

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Title An empirical study of encoding schemes and search strategies in discovering causal networks
Author(s) Dai, H.
Li, Gang
Tu, Yiqing
Conference name Machine learning : ECML 2002 : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002 : proceedings
Conference location Helsinki, Finland
Conference dates 19-23 August 2002
Title of proceedings Machine Learning: ECML 2002: Proceedings of the 13th European Conference on Machine Learning
Editor(s) Elomaa, Tapio
Mannila, H.
Toivonen, H.
Publication date 2002
Series Lecture notes in computer science ; 2430.
Start page 48
End page 59
Publisher Springer Berlin
Place of publication Berlin, Germany
Summary Efficiently inducing precise causal models accurately reflecting given data sets is the ultimate goal of causal discovery. The algorithm proposed by Wallace et al. [10] has demonstrated its ability in discovering Linear Causal Models from data. To explore the ways to improve efficiency, this research examines three different encoding schemes and four searching strategies. The experimental results reveal that (1) specifying parents encoding method is the best among three encoding methods we examined; (2) In the discovery of linear causal models, local Hill climbing works very well compared to other more sophisticated methods, like Markov Chain Monte Carto (MCMC), Genetic Algorithm (GA) and Parallel MCMC searching.

ISBN 3540440364
9783540440369
ISSN 0302-9743
1611-3349
Language eng
Field of Research 080105 Expert Systems
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2002 Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004877

Document type: Conference Paper
Collection: School of Information Technology
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