Abstract
We present a semi–parametric approach to evaluate the reliability of rules obtained from a rough set information system by replacing strict determinacy by predicting a random variable which is a mixture of latent probabilities obtained from repeated measurements of the decision variable. It is demonstrated that the algorithm may be successfully used for unsupervised learning.
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Düntsch, I., Gediga, G. (2008). Probabilistic Granule Analysis. In: Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Rough Sets and Current Trends in Computing. RSCTC 2008. Lecture Notes in Computer Science(), vol 5306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88425-5_23
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DOI: https://doi.org/10.1007/978-3-540-88425-5_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88423-1
Online ISBN: 978-3-540-88425-5
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