Data classification using the Dempster-Shafer method
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
Taylor & Francis
2014
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/3346/ |
| _version_ | 1848791005928620032 |
|---|---|
| author | Chen, Qi Whitbrook, Amanda Aickelin, Uwe Roadknight, Chris |
| author_facet | Chen, Qi Whitbrook, Amanda Aickelin, Uwe Roadknight, Chris |
| author_sort | Chen, Qi |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:21:38Z |
| format | Article |
| id | nottingham-3346 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:21:38Z |
| publishDate | 2014 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-33462020-05-04T16:42:43Z https://eprints.nottingham.ac.uk/3346/ Data classification using the Dempster-Shafer method Chen, Qi Whitbrook, Amanda Aickelin, Uwe Roadknight, Chris 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. Taylor & Francis 2014-02-26 Article PeerReviewed 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 Data Mining Dempster–Shafer theory data classification Dempster's rule of combination http://dx.doi.org/10.1080/0952813X.2014.886301 doi:10.1080/0952813X.2014.886301 doi:10.1080/0952813X.2014.886301 |
| spellingShingle | Data Mining Dempster–Shafer theory data classification Dempster's rule of combination Chen, Qi Whitbrook, Amanda Aickelin, Uwe Roadknight, Chris Data classification using the Dempster-Shafer method |
| title | Data classification using the Dempster-Shafer method |
| title_full | Data classification using the Dempster-Shafer method |
| title_fullStr | Data classification using the Dempster-Shafer method |
| title_full_unstemmed | Data classification using the Dempster-Shafer method |
| title_short | Data classification using the Dempster-Shafer method |
| title_sort | data classification using the dempster-shafer method |
| topic | Data Mining Dempster–Shafer theory data classification Dempster's rule of combination |
| url | https://eprints.nottingham.ac.uk/3346/ https://eprints.nottingham.ac.uk/3346/ https://eprints.nottingham.ac.uk/3346/ |