Abstract
In real-world applications, mathematical models must often deal with values that are missing or undefined. The aim of this paper is to provide a survey on types and reasons for such non-availability. It motivates the need to handle different reasons for missingness in a different, but appropriate way. In particular, non-existence, ignorance, and inconsistency are studied. The paper also presents a novel way of how to compute with different types of missing values at the same time.
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Authors acknowledge support by project “LQ1602 IT4Innovations excellence in science” and by GAČR 16-19170S.
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Burda, M., Murinová, P., Pavliska, V. (2019). On Missing Membership Degrees: Modelling Non-existence, Ignorance and Inconsistency. In: Destercke, S., Denoeux, T., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Uncertainty Modelling in Data Science. SMPS 2018. Advances in Intelligent Systems and Computing, vol 832. Springer, Cham. https://doi.org/10.1007/978-3-319-97547-4_4
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DOI: https://doi.org/10.1007/978-3-319-97547-4_4
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