On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure

DOI

10.2991/eusflat.2011.51How to use a DOI?

Keywords

evolving fuzzy models, incremental learning, regression, fuzzy inclusion, rule merging, fuzzy set merging, complexity reduction

Abstract

This paper tackles the problem of complexity reduction in evolving fuzzy regression models of the Takagi-Sugeno type. The incremental model adaptation process used to evolve such models over time, often produces redundancies such as overlapping rule antecedents. We propose the use of a fuzzy inclusion measure in order to detect such redundancies as well as a procedure for merging rules that are sufficiently similar. Experimental studies with two high-dimensional real-world data sets provide evidence for the effectiveness of our approach; it turns out that a reduction in complexity is even accompanied by an increase in predictive accuracy.

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/).

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TY  - CONF
AU  - Edwin Lughofer
AU  - Eyke Hüllermeier
PY  - 2011/08
DA  - 2011/08
TI  - On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure
BT  - Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)
PB  - Atlantis Press
SP  - 380
EP  - 387
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.51
DO  - 10.2991/eusflat.2011.51
ID  - Lughofer2011/08
ER  -