Abstract
This work introduces a generalization of the algorithm LEM3, an incremental learning system of production rules from examples, based on the Boolean Approximation Space introduced by Pawlak. The generalization is supported in the Stochastic Approximation Space introduced by Wong and Ziarko. In this paper, stochastic limits in the precision of the upper and lower approximations of a class are addressed. These allow the generation of certain rules with a certainty level β(0.5≤β≤1) Also the modifications in LEM3 necessary in order to handle examples with missing attribute values are introduced.
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References
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© 2001 Springer-Verlag Berlin Heidelberg
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Fernández-Baizán, M.C., Pérez-Llera, C., Feito-García, J., Almeida, A. (2001). LEM3 Algorithm Generalization Based on Stochastic Approximation Space. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_34
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DOI: https://doi.org/10.1007/3-540-45554-X_34
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