Abstract
It has been a focus in the area of Intelligent System how to solve the inferential problem more accurately and convenient with the aid of AI. In this paper, some novel concepts as Key Factor, Key Associative Factor and Certainty Forecast are initiated. In addition, we pioneer a method so-called Uncertainty Reason based on Key-Associative Certainty Forecast, into which an innovative forecast mechanism is proposed and integrated. Study of cases and experiment statistics makes it clear that our scenario be practical , effective, and with an accurate rate of diagnosis over 90%. It can extend capability of diagnosing by the digitalized knowledge rules in KDB. On the whole, the favorable macro performance demonstrates it be a promising reason scheme.
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© 2011 Springer-Verlag Berlin Heidelberg
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Tan, W., Wang, X., Xi, J. (2011). An Intelligent Diagnosing System by the Uncertainty Reason Based on Key-Associative Certainty Forecast. In: Lin, S., Huang, X. (eds) Advances in Computer Science, Environment, Ecoinformatics, and Education. CSEE 2011. Communications in Computer and Information Science, vol 217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23339-5_39
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DOI: https://doi.org/10.1007/978-3-642-23339-5_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23338-8
Online ISBN: 978-3-642-23339-5
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