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An empirical study of encoding schemes and search strategies in discovering causal networks
conference contribution
posted on 2002-01-01, 00:00 authored by Honghua Dai, Gang LiGang Li, Yiqing TuEfficiently 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.
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Title of proceedings
Machine Learning: ECML 2002: Proceedings of the 13th European Conference on Machine LearningEvent
Machine learning : ECML 2002 : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002 : proceedingsSeries
Lecture notes in computer science ; 2430.Pagination
48 - 59Publisher
Springer BerlinLocation
Helsinki, FinlandPlace of publication
Berlin, GermanyPublisher DOI
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Start date
2002-08-19End date
2002-08-23ISSN
0302-9743eISSN
1611-3349ISBN-13
9783540440369ISBN-10
3540440364Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2002 Springer-Verlag Berlin HeidelbergEditor/Contributor(s)
T Elomaa, H Mannila, H ToivonenUsage metrics
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