Authors: G. Golino, A. Graziano, A. Farina, W. Mellano, F. Ciaramaglia
Scope of this paper is to investigate the performances of different identity declaration fusion algorithms in terms of probability of correct classification, supposing that the information for combination of the inferences from the different classifier is affected by measurement errors. In particular, these information have been assumed to be provided in the form of confusion matrices. Six identity fusion algorithms from literature with different complexity have been included in the comparison: heuristic methods such as voting and Borda Count, Bayes’ and Dempster-Shafer’s methods and the Proportional Redistribution Rule n° 1 in the Dempster-Shafer’s framework.
Comments: 7 Pages.
Download: PDF
[v1] 2014-11-20 01:17:43
Unique-IP document downloads: 69 times
Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.
Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.