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    Data classification using the Dempster–Shafer method.

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    Authors
    Chen, Qi
    Whitbrook, Amanda cc
    Aickelin, Uwe
    Roadknight, Chris
    Affiliation
    University of Nottingham
    Issue Date
    2014-02-26
    
    Metadata
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    Abstract
    In this paper, the Dempster–Shafer (D–S) method is used as the theoretical basis for creating data classification systems. Testing is carried out using three popular multiple attribute benchmark data-sets that have two, three and four classes. In each case, a subset of the available data is used for training to establish thresholds, limits or likelihoods of class membership for each attribute, and hence create mass functions that establish probability of class membership for each attribute of the test data. Classification of each data item is achieved by combination of these probabilities via Dempster’s rule of combination. Results for the first two data-sets show extremely high classification accuracy that is competitive with other popular methods. The third data-set is non-numerical and difficult to classify, but good results can be achieved provided the system and mass functions are designed carefully and the right attributes are chosen for combination. In all cases, the D–S method provides comparable performance to other more popular algorithms, but the overhead of generating accurate mass functions increases the complexity with the addition of new attributes. Overall, the results suggest that the D–S approach provides a suitable framework for the design of classification systems and that automating the mass function design and calculation would increase the viability of the algorithm for complex classification problems.
    Citation
    Chen, Q. et al (2014) 'Data classification using the Dempster–Shafer method', Journal of Experimental & Theoretical Artificial Intelligence, 26 (4):493-517 .
    Publisher
    Taylor and Francis
    Journal
    Journal of Experimental & Theoretical Artificial Intelligence
    URI
    http://hdl.handle.net/10545/622385
    DOI
    10.1080/0952813X.2014.886301
    Additional Links
    http://www.tandfonline.com/doi/abs/10.1080/0952813X.2014.886301
    Type
    Article
    Language
    en
    ISSN
    0952813X
    EISSN
    13623079
    ae974a485f413a2113503eed53cd6c53
    10.1080/0952813X.2014.886301
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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