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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the EUSFLAT 2011 conference |
| Pages | 380-387 |
| Number of pages | 8 |
| Edition | 1 |
| DOIs | |
| Publication status | Published - 2011 |
Fields of science
- 101001 Algebra
- 101 Mathematics
- 102 Computer Sciences
- 101013 Mathematical logic
- 101020 Technical mathematics
- 102001 Artificial intelligence
- 102003 Image processing
- 202027 Mechatronics
- 101019 Stochastics
- 211913 Quality assurance
JKU Focus areas
- Computation in Informatics and Mathematics
- Mechatronics and Information Processing
- Nano-, Bio- and Polymer-Systems: From Structure to Function