title: |
On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure |
|
publication: |
||
part of series: |
Advances in Intelligent Systems Research | |
| pages: | 380 - 387 | |
DOI: |
To be assigned soon (how to use a DOI) | |
author(s): |
Edwin Lughofer, Eyke Hüllermeier |
|
publication date: |
July 2011 |
|
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: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
|
full text: |