DocumentCode
2542163
Title
Online causal discovery
Author
Yu, Kui ; Wu, Xindong ; Wang, Hao
Author_Institution
Dept. of Comput. Sci., Hefei Univ. of Technol., Hefei, China
fYear
2010
fDate
7-9 July 2010
Firstpage
667
Lastpage
671
Abstract
The standard causal discovery assumes that all variables are available from the beginning. In this paper, we consider an untouched scenario in which not all variables are available in advance. We call this scenario online causal discovery which assumes that the target of interest is given in advance while the other variables are unknown. With this situation, an online algorithm is presented which consists of two phases: online growing and online shrinking phase. Experimental results validate our algorithms compared with a state-of-the-art standard algorithm of causal discovery.
Keywords
belief networks; learning (artificial intelligence); Bayesian network; online causal discovery algorithm; online growing phase; online shrinking phase; Algorithm design and analysis; Bayesian methods; Classification algorithms; Heuristic algorithms; Markov processes; Measurement; Probability distribution; Bayesian network; causal discovery; online causal discovery;
fLanguage
English
Publisher
ieee
Conference_Titel
Cognitive Informatics (ICCI), 2010 9th IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-8041-8
Type
conf
DOI
10.1109/COGINF.2010.5599825
Filename
5599825
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