A Divergence Measure Between Mass Functions

Jianbing Ma

doi:10.1007/978-3-319-25159-2_5

A Divergence Measure Between Mass Functions

Jianbing Ma

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Evidence theory is widely used in data mining, machine learning, clustering and database systems. In these applications, often combination of mass functions is performed without checking the degree of consistency between the mass functions, which may lead to counterintuitive results. In this paper, we aim to measure the divergences among mass functions which can hence prevent highly inconsistent mass functions from been combined. To this end, we propose a divergence measure between two mass functions. In addition, incompleteness measures and similarity measures are also provided based on divergence measures.

Original language	English
Title of host publication	Knowledge Science, Engineering and Management: 8th International Conference, Chongqing, China, October 28-30 2015, Proceedings
Editors	Songmao Zhang, Martin Wirsing, Zili Zhang
Place of Publication	Switzerland
Publisher	Springer Verlag
Pages	53-65
Volume	9403
ISBN (Print)	Online: 978-3-319-25159-2, Print: 978-3-319-25158-5
DOIs	https://doi.org/10.1007/978-3-319-25159-2_5
Publication status	Published - 3 Nov 2015

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10.1007/978-3-319-25159-2_5

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A Divergence Measure Between Mass Functions

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