Controlling asymmetric errors in neuro-fuzzy classification
Aljoscha Klose, Rudolf Kruse, Karsten Schulz, Ulrich Thönnessen · 2000
In many practical classification problems the severeness of misclassifications depends on the semantics of true and predicted class in the underlying domain.We present such a problem from machine vision, where additionally the class probabilities are extremely unbalanced.Due to their interpretability neuro-fuzzy classifiers axe a popular way to extract rules from example data.However, like most classifter approaches, neuro-fuzzy systems have problems when learning from asymmetric or unbalanced data.We present modifications to the learning algorithm of the NEFCLASS system to cope with these problems, and show experimental results.