Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-11)

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

Authors
Edwin Lughofer, Eyke Hüllermeier
Corresponding Author
Edwin Lughofer
Available Online August 2011.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology
Part of series
Advances in Intelligent Systems Research
Publication Date
August 2011
ISBN
978-90-78677-00-0
ISSN
1951-6851
DOI
https://doi.org/10.2991/eusflat.2011.51How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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
PB  - Atlantis Press
SP  - 380
EP  - 387
SN  - 1951-6851
UR  - https://doi.org/10.2991/eusflat.2011.51
DO  - https://doi.org/10.2991/eusflat.2011.51
ID  - Lughofer2011/08
ER  -