@misc{10481/64975,
year = {2010},
url = {http://hdl.handle.net/10481/64975},
abstract = {Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule Based Classification Systems (FRBCSs) are
a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users.
The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of IntervalValued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning
step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the
problem.
We analyze the goodness of this approach using two basic and well-known
fuzzy rule learning algorithms, the Chi et al.’s method and the Fuzzy Hybrid
Genetics-Based Machine Learning algorithm. We show the improvement
achieved by this model through an extensive empirical study with a large
collection of data-sets.},
organization = {Spanish Government

TIN2008-06681-C06-01
TIN2007-65981},
publisher = {ELSEVIER},
keywords = {Fuzzy rule based classification systems},
keywords = {IntervalValued Fuzzy Sets},
keywords = {Tuning},
keywords = {Genetic algorithms},
title = {Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets and Genetic Amplitude Tuning},
doi = {10.1016/j.ins.2010.06.018},
author = {Sanz, José Antonio and Fernández, Alberto and Bustince, Humberto and Herrera Triguero, Francisco},
}