On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure
Edwin David Lughofer, Eyke Hüllermeier · 2011
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.