'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
In this paper a combination of neuro-fuzzy
classifiers for improved classification performance and reliability
is considered. A general fuzzy min-max (GFMM) classifier with
agglomerative learning algorithm is used as a main building
block. An alternative approach to combining individual classifier
decisions involving the combination at the classifier model level is
proposed. The resulting classifier complexity and transparency is
comparable with classifiers generated during a single crossvalidation
procedure while the improved classification
performance and reduced variance is comparable to the ensemble
of classifiers with combined (averaged/voted) decisions. We also
illustrate how combining at the model level can be used for
speeding up the training of GFMM classifiers for large data sets