Towards Evolving Parametric Fuzzy Classifiers Using a Virtual Sample Generation Approach
Holger Hähnel, Arne-Jens Hempel, Gernot Herbst
Available Online June 2015.
- 10.2991/ifsa-eusflat-15.2015.157How to use a DOI?
- Fuzzy classification, evolving classifier, virtual sample generation.
Evolving classification models are designed to solve online tasks with demands restricting computational power and memory. The present paper proposes an evolving version of an established fuzzy classification approach based on fuzzy pattern classes. The approach incorporates a novel type of virtual sample generation. It creates examples from given parametric model information and thus inverts the classifier’s batch learning algorithm. In an evolving environment, virtual examples and real learning data are combined for on-line learning. The main advantage of this approach is that the original learning process retains its applicability while memory demands are reduced significantly. Academic examples demonstrate the feasibility.
- © 2015, 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/).
Cite this article
TY - CONF AU - Holger Hähnel AU - Arne-Jens Hempel AU - Gernot Herbst PY - 2015/06 DA - 2015/06 TI - Towards Evolving Parametric Fuzzy Classifiers Using a Virtual Sample Generation Approach BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 1111 EP - 1118 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.157 DO - 10.2991/ifsa-eusflat-15.2015.157 ID - Hähnel2015/06 ER -