Towards Evolving Parametric Fuzzy Classifiers Using a Virtual Sample Generation Approach

DOI

10.2991/ifsa-eusflat-15.2015.157How to use a DOI?

Keywords

Fuzzy classification, evolving classifier, virtual sample generation.

Abstract

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.

Copyright

© 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/).

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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  -