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Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
ISBN: 978-1-4673-5164-5
pp: 629-634
ABSTRACT
This paper presents a Minimal Causal Model Inducer that can be used for the reliable knowledge discovery. The minimal-model semantics of causal discovery is an essential concept for the identification of a best fitting model in the sense of satisfactory consistent with the given data and be the simpler, less expressive model. Consistency is one of major measures of reliability in knowledge discovery. Therefore to develop an algorithm being able to derive a minimal model is an interesting topic in the are of reliable knowledge discovery. various causal induction algorithms and tools developed so far can not guarantee that the derived model is minimal and consistent. It was proved the MML induction approach introduced by Wallace, Keven and Honghua Dai is a minimal causal model learner. In this paper, we further prove that the developed minimal causal model learner is reliable in the sense of satisfactory consistency. The experimental results obtained from the tests on a number of both artificial and real models provided in this paper confirm this theoretical result.
INDEX TERMS
Data models, Reliability theory, Encoding, Data mining, Probability distribution, Minimal Model Learner; reliability; data mining
CITATION
Honghua Dai, Sarah Keble-Johnston, Min Gan, "Reliable Knowledge Discovery with a Minimal Causal Model Inducer", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 629-634, doi:10.1109/ICDMW.2012.145
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