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Learning Fuzzy Measures

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Discrete Fuzzy Measures

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 382))

Abstract

This chapter is a key contribution of this work in which various computational approaches to learning fuzzy measures are described. The learning problem is framed from the perspective of data fitting, where we aim to define a model that interpolates or approximates a set of observed or user-specified instances. Fitting is performed with respect to different metrics, and by solving different convex and non-convex optimisation problems. The computational complexity of fuzzy measures is addressed by using simplifying assumptions, in particular the notion of k-order fuzzy measures and the software packages implementing the presented fitting approaches are also described.

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Notes

  1. 1.

    Available at https://github.com/murillojavieriv/khlms.

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Authors and Affiliations

  1. Deakin University, Burwood, VIC, Australia

    Gleb Beliakov & Simon James

  2. Ningbo University, Ningbo, Zhejiang, China

    Jian-Zhang Wu

Authors
  1. Gleb Beliakov
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  2. Simon James
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  3. Jian-Zhang Wu
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Correspondence to Gleb Beliakov .

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Beliakov, G., James, S., Wu, JZ. (2020). Learning Fuzzy Measures. In: Discrete Fuzzy Measures. Studies in Fuzziness and Soft Computing, vol 382. Springer, Cham. https://doi.org/10.1007/978-3-030-15305-2_8

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