Abstract
The link between information theory and fuzzy logic has been proven in several previous papers. From this starting point, we propose here a review about the concept of divergence measures, which was proposed as a tool for comparing two fuzzy sets. The initial definition comes from the ideas behind the classical concept of divergence between two probability distributions. Following a path similar to the one considered to obtain fuzziness measures from uncertainty measures, we are able to define fuzzy divergences. Apart from that, some possible generalizations are considered.
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Acknowledgements
We would like to acknowledge the help and support of Prof. Gil to initiate us to the wonderful world of research. He used to say he was our scientific father and we are honored he really was.
From the economical point of view, this work was partially supported by the research project TIN2014-59543-P (Spain).
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Montes, S., Díaz, S., Martinetti, D. (2018). Divergence Measures: From Uncertainty to Imprecision. In: Gil, E., Gil, E., Gil, J., Gil, M. (eds) The Mathematics of the Uncertain. Studies in Systems, Decision and Control, vol 142. Springer, Cham. https://doi.org/10.1007/978-3-319-73848-2_62
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DOI: https://doi.org/10.1007/978-3-319-73848-2_62
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