Abstract
A random set semantics for imprecise concepts is introduced. It is then demonstrated how label prototypes describing data sets can be learnt in this framework. These prototypes take the form of vectors of mass assignments showing the distribution of appropriate labels across the database for various attributes. The potential of this approach for fusion, classification and query evaluation is then investigated.
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Lawry, J. (2003). Random Sets and Appropriateness Degrees for Modelling with Labels. In: Lawry, J., Shanahan, J., L. Ralescu, A. (eds) Modelling with Words. Lecture Notes in Computer Science(), vol 2873. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39906-3_10
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DOI: https://doi.org/10.1007/978-3-540-39906-3_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20487-9
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