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Parameter learning and applications of the inclusion-exclusion integral for data fusion and analysis

journal contribution
posted on 2020-01-01, 00:00 authored by A Honda, Simon JamesSimon James
© 2019 Elsevier B.V. Developments in the learning and interpretation of fuzzy integrals have paved the way for a myriad of applications in data analysis and prediction. The ability of the associated fuzzy measure to model heterogeneous interactions allow high flexibility when it comes to data fusion tasks – comparable to that of neural networks – however the fuzzy integral structure and properties also afford a degree of robustness and interpretability not enjoyed by such tools. On the other hand, neural network architectures can accommodate fuzzy integrals as a special case. In this paper, we propose that such a representation allows us to naturally extend and adapt the fuzzy integral framework toward specific applications. We focus on the inclusion-exclusion integral, which is a generalization of the Choquet integral, and detail methods for learning the various parameters, given its extended architecture. We then validate the performance and usefulness of this approach on some benchmark datasets.

History

Journal

Information Fusion

Volume

56

Pagination

28 - 38

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

1566-2535

Language

eng

Publication classification

C1 Refereed article in a scholarly journal