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Linear causal model discovery using the MML criterion
conference contribution
posted on 2002-01-01, 00:00 authored by Gang LiGang Li, Honghua Dai, Yiqing TuDetermining the causal structure of a domain is a key task in the area of Data Mining and Knowledge Discovery.The algorithm proposed by Wallace et al. [15] has demonstrated its strong ability in discovering Linear Causal Models from given data sets. However, some experiments showed that this algorithm experienced difficulty in discovering linear relations with small deviation, and it occasionally gives a negative message length, which should not be allowed. In this paper, a more efficient and precise MML encoding scheme is proposed to describe the model structure and the nodes in a Linear Causal Model. The estimation of different parameters is also derived. Empirical results show that the new algorithm outperformed the previous MML-based algorithm in terms of both speed and precision.
History
Title of proceedings
Proceedings of the 2002 IEEE International Conference on Data MiningEvent
Institute of Electrical and Electronics Engineers. Conference (2002: Maebishi-shi, Japan)Pagination
274 - 281Publisher
IEEE Computer SocietyLocation
Maebishi-shi, JapanPlace of publication
Los Alamitos, Calif.Start date
2002-12-09End date
2002-12-12ISBN-13
9780769517544ISBN-10
0769517544Language
engPublication classification
E1 Full written paper - refereed; E Conference publicationCopyright notice
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Editor/Contributor(s)
V Kumar, S Tsumoto, N Zhong, P Yu, X WuUsage metrics
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