Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition
Information Sciences: an International Journal - Special issue on advanced neuro-fuzzy techniques and their applications
NEFCLASSmdash;a neuro-fuzzy approach for the classification of data
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
A fuzzy neural network for pattern classification and feature selection
Fuzzy Sets and Systems
Neural Networks vs Logistic Regression: a Comparative Study on a Large Data Set
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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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.