Data classification using the Dempster-Shafer method

Chen, Qi, Whitbrook, Amanda, Aickelin, Uwe and Roadknight, Chris (2014) Data classification using the Dempster-Shafer method. Journal of Experimental & Theoretical Artificial Intelligence . pp. 1-25. ISSN 0952-813X

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In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems. Testing is carried out using three popular (multiple attribute) benchmark datasets 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 datasets show extremely high classification accuracy that is competitive with other popular methods. The third dataset 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 Dempster-Shafer 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.

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis Group in Journal of Experimental & Theoretical Artificial Intelligence on 26/02/2014, available online:
Keywords: Data, Mining, Dempster–Shafer theory, data classification, Dempster's rule of combination
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number:
Depositing User: Aickelin, Professor Uwe
Date Deposited: 29 Sep 2014 21:15
Last Modified: 04 May 2020 16:42

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