Controlling Asymmetric Errors in Neuro-Fuzzy Classification

  title={Controlling Asymmetric Errors in Neuro-Fuzzy Classification},
  author={Aljoscha Klose and Rudolf Kruse and Karsten Schulz and Ulrich Thoennessen},
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… CONTINUE READING

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