Constantinidis, A.S., Fairhurst, Michael, Rahman, Ahmad Fuad Rezaur (2001) A New Multi-Expert Decision Combination Algorithm and its Application to the Detection of Circumscribed Masses in Digital Mammograms. Pattern Recognition, 34 . pp. 1527-1537. ISSN 0031-3203. (doi:10.1016/S0031-3203(00)00088-1) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:467)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
| Official URL: http://dx.doi.org/10.1016/S0031-3203(00)00088-1 |
Abstract
A new multiple expert fusion algorithm is introduced, designated the “augmented behaviour-knowledge space method”. Most existing multiple expert classification methods rely on a large training dataset in order to be properly utilised. The proposed method effectively overcomes this problem as it exploits the confidence levels of the decisions of each classifier. It will be shown that this new approach is advantageous when small datasets are available, and this is illustrated in its application to the detection of circumscribed masses in digital mammograms, with very encouraging results.
| Item Type: | Article |
|---|---|
| DOI/Identification number: | 10.1016/S0031-3203(00)00088-1 |
| Subjects: | T Technology |
| Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
| Depositing User: | J. Harries |
| Date Deposited: | 19 Dec 2007 18:16 UTC |
| Last Modified: | 05 Nov 2024 09:30 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/467 (The current URI for this page, for reference purposes) |
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