A Divergence Measure Between Mass Functions

Jianbing Ma

    Research output: Chapter in Book/Report/Conference proceedingChapter


    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 languageEnglish
    Title of host publicationKnowledge Science, Engineering and Management: 8th International Conference, Chongqing, China, October 28-30 2015, Proceedings
    EditorsSongmao Zhang, Martin Wirsing, Zili Zhang
    Place of PublicationSwitzerland
    PublisherSpringer Verlag
    ISBN (Print)Online: 978-3-319-25159-2, Print: 978-3-319-25158-5
    Publication statusPublished - 3 Nov 2015

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