Incorporating Dynamic Uncertainties into a Fuzzy Classifier

DOI

10.2991/eusflat.2011.4How to use a DOI?

Keywords

fuzzy classifier, uncertainty, trust management

Abstract

Dealing with classification problems in practice often has to cope with uncertain information, either in the training or in the operation phase or both. Modeling these uncertainties allows to enhance the robustness or performance of the classifier. In this paper we focus on the operation phase and present a general, but simple extension to rule based fuzzy classifier to do so. Therefor uncertain features are gradually and dimension wise faded out of the classification process. An artificial two­dimensional dataset is used to visualize the effectiveness of this approach. Investigations on three benchmark datasets shows the performance and gain in robustness.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Jens Hülsmann
AU  - Andreas Buschermohle
AU  - Werner Brockmann
PY  - 2011/08
DA  - 2011/08
TI  - Incorporating Dynamic Uncertainties into a Fuzzy Classifier
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 388
EP  - 395
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.4
DO  - 10.2991/eusflat.2011.4
ID  - Hülsmann2011/08
ER  -