On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure
Edwin Lughofer, Eyke Hüllermeier
Available Online August 2011.
- https://doi.org/10.2991/eusflat.2011.51How to use a DOI?
- evolving fuzzy models, incremental learning, regression, fuzzy inclusion, rule merging, fuzzy set merging, complexity reduction
- 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.
- 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 -