Abstract
Rough approximations, which consist of lower and upper approximations, are described under objects characterized by possibilistic information that is expressed by a normal possibility distribution. Concepts of not only possibility but also certainty are used to construct an indiscernibility relation. First, rough approximations are shown for a set of discernible objects by using the indiscernibility relation. Next, a set of objects characterized by possibilistic information is approximated. Consequently, rough approximations consist of objects with a degree expressed by an interval value where lower and upper degrees mean the lower and the upper bounds of the actual degree. This leads to the complementarity property linked with lower and upper approximations in the case of a set of discernible objects, as is valid under complete information. Furthermore, a criterion is introduced to judge whether or not an object is regarded as supporting rules. By using the criterion, we can select only objects that are regarded as inducing rules.
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Nakata, M., Sakai, H. (2014). An Approach Based on Rough Sets to Possibilistic Information. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-08852-5_7
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DOI: https://doi.org/10.1007/978-3-319-08852-5_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08851-8
Online ISBN: 978-3-319-08852-5
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