Data classification using the Dempster-Shafer method

Qi Chen, Amanda Whitbrook, Uwe Aickelin, Chris Roadknight

Research output: Journal PublicationArticlepeer-review

26 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)493-517
Number of pages25
JournalJournal of Experimental and Theoretical Artificial Intelligence
Volume26
Issue number4
DOIs
Publication statusPublished - 2 Oct 2014
Externally publishedYes

Keywords

  • Dempster's rule of combination
  • Dempster-Shafer theory
  • data classification

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence

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