Abstract
In this paper, we develop the conditional evidence theory and apply it to knowledge discovery in database. In this theory, we assume that a priori knowledge about generic situation and evidence about situation at hand can be modelled by two independent random sets. Dempster’s rule of combination is a popular method used in evidence theory, we think that this rule can be applied to knowledge revision, but isn’t appropriate for knowledge updating. Based on random set theory, we develop a new bayesian updating rule in evidence theory. More importantly, we show that bayesian updating rule can be performed incrementally by using Möbius transforms.
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Tang, Y., Sun, S., Liu, Y. (2004). Conditional Evidence Theory and Its Application in Knowledge Discovery. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24655-8_54
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DOI: https://doi.org/10.1007/978-3-540-24655-8_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21371-0
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