Openly accessible

Discovering linear causal model from incomplete data

Li, Gang, Dai, Honghua and Tu, Y. 2003, Discovering linear causal model from incomplete data, in InTech'03 : Proceedings of the Fourth International Conference on Intelligent Technologies 2003, Chiang Mai University, Institute for Science and Technology Research and Development, Chiang Mai, Thailand, pp. 1-11.

Attached Files
Name Description MIMEType Size Downloads
dai-discoveringlinear-2003.pdf Published version application/pdf 180.19KB 25

Title Discovering linear causal model from incomplete data
Author(s) Li, Gang
Dai, Honghua
Tu, Y.
Conference name International Conference on Intelligent Technologies (4th : 2003, Thailand)
Conference location Chiang Mai Plaza, Thailand
Conference dates 17-19 December 2003
Title of proceedings InTech'03 : Proceedings of the Fourth International Conference on Intelligent Technologies 2003
Editor(s) Dhompongsa, Sompong
Publication date 2003
Conference series International Conference on Intelligent Technologies
Start page 1
End page 11
Total pages 11
Publisher Chiang Mai University, Institute for Science and Technology Research and Development
Place of publication Chiang Mai, Thailand
Summary One common drawback in algorithms for learning Linear Causal Models is that they can not deal with incomplete data set. This is unfortunate since many real problems involve missing data or even hidden variable. In this paper, based on multiple imputation, we propose a three-step process to learn linear causal models from incomplete data set. Experimental results indicate that this algorithm is better than the single imputation method (EM algorithm) and the simple list deletion method, and for lower missing rate, this algorithm can even find models better than the results from the greedy learning algorithm MLGS working in a complete data set. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.
Notes
Every reasonable effort has been made to ensure that permission has been obtained for items included in Deakin Research Online. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au

ISBN 9746581511
9789746581516
Language eng
Field of Research 080105 Expert Systems
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2003, InTech
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005210

Document type: Conference Paper
Collections: School of Information Technology
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Access Statistics: 408 Abstract Views, 25 File Downloads  -  Detailed Statistics
Created: Mon, 07 Jul 2008, 09:46:49 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.