Learning Fuzzy Classification Rules from Data
Hans Roubos, MAGNE SETNES, János Abonyi · 2001
Automatic design of fuzzy rule-based classification systems based on labeled data is considered. It is recognized that both classification performance and interpretability are of major importance and effort is made to keep the resulting rule bases small and comprehensible. An iterative approach for developing fuzzy classifiers is proposed. The initial model is derived from the data and subsequently, feature selection and rule base simplification are applied to reduce the model, and a GA is used for model tuning. An application to the Wine data classification problem is shown. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.