Edwin Lughofer, Eyke Hüllermeier,
"On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure"
: Proceedings of the EUSFLAT 2011 conference, Seite(n) 380-387, 2011
Original Titel:
On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the EUSFLAT 2011 conference
Original Kurzfassung:
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.