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This paper presents a model for the treatment of skewness effects on the accuracy of the NEFCLASS classifier by changing the embedded discretization method within the classifier. NEFCLASS is a common example of the construction of a neuro-fuzzy system. The popular NEFCLASS classifier exhibits surprising behaviour when the feature values of the training and testing data sets exhibit significant skew. As skewed feature values are commonly observed in biological data sets, this is a topic that is of interest in terms of the applicability of such a classifier to these types of problems. From this study it is clear that the effect of skewness on classification accuracy is significant, and that this must be considered in work dealing with skewed data distributions. We compared accuracy of NEFCLASS classifier with two modified versions of NEFCLASS embedded with MME and CAIM discretization methods. From this study it is found the CAIM and MME discretization methods results in greater improvements in the classification accuracy of NEFCLASS as compared to using the original EQUAL-WIDTH technique NEFCLASS uses by default.

Classification Confusion within Nefclass Caused by Feature Value Skewness in Multi-dimensional Datasets