Abstract
In a probabilistic rough set model, the positive, negative and boundary regions are associated with classification errors or uncertainty. The uncertainty is controlled by a pair of thresholds defining the three regions. The problem of searching for optimal thresholds can be formulated as the minimization of uncertainty induced by the three regions. By using Shannon entropy as a measure of uncertainty, we present an information-theoretic approach to the interpretation and determination of thresholds.
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Deng, X., Yao, Y. (2012). An Information-Theoretic Interpretation of Thresholds in Probabilistic Rough Sets. 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_46
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DOI: https://doi.org/10.1007/978-3-642-31900-6_46
Publisher Name: Springer, Berlin, Heidelberg
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