Affiliations: Center for Informatics – CIn, Federal University of
Pernambuco, UFPE, Recife, PE, Brazil | Post-Graduation Program in Knowledge Management and
Information Technology, Catholic University of Brasilia, UCB, Brasília,
DF, Brazil
Abstract: Hybrid Neural Systems that integrate symbolic algorithms or
fuzzysystems to Artificial Neural Networks (ANN) are a potential alternativeto
the more traditional ANN models. However, in contrast with the ANNmodels, these
systems have not been yet fully explored from a practicalviewpoint to show
their effectiveness in large scale applications. Thispaper presents an
extensive comparative analysis of the neuro-fuzzymodels FWD (Feature-Weighted
Detector) and FuNN (Fuzzy Neural Network),together with their rule extraction
techniques in a large-scale problem.Two aspects are considered: generalization
performance of the models,and the interpretation and explanation qualities of
the extractedknowledge. The experiments are conducted in the context of a large
scalecredit risk assessment application in a real-world operation of aBrazilian
financial institution. The results attained are compared tothose observed with
multi-layer perceptron networks.
