Abstract
In the previous decision-theoretic rough sets model (DTRS), its loss function values are precise. This paper extends the precise values of loss functions to a more realistic stochastic environment. Considering all loss functions in DTRS model obey a certain of probabilistic distribution, the extension of decision-theoretic rough set models under uniform distribution and normal distribution are proposed in this paper. An empirical study validates the reasonability and effectiveness of the proposed approach.
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Liu, D., Li, T., Liang, D. (2012). Decision-Theoretic Rough Sets with Probabilistic Distribution. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_48
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DOI: https://doi.org/10.1007/978-3-642-31900-6_48
Publisher Name: Springer, Berlin, Heidelberg
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