A Synergistic Approach to Enhance the Accuracy-interpretability Trade-off of the NECLASS Classifier for Skewed Data Distribution

NEFCLASS is a common example of a neuro-fuzzy system. The popular NEFCLASS classifier exhibits surprising behaviour when the feature values of the training and testing datasets exhibit significant skew. This paper presents a combined approach to improve the classification accuracy and interpretability of the NEFCLASS classifier, when data distribution exhibits positive skewness. The proposed model consists of two steps. Firstly, we used an alternative discretization method to initialize fuzzy sets. Secondly, we devised a statistical rule pruning algorithm based on adjusted residual to reduce the number of rules, thus improving interpretability. This method improves the interpretability of NEFCLASS without significant accuracy deterioration. Moreover, a hybrid approach combining the two approaches is developed to increase the accuracy-interpretability trade-off of NEFCLASS.