Abstract
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. This paper presents an approach to improve the classification accuracy of the Nefclass classifier, when data distribution exhibits positive skewness. The Nefclass classifier is extended to provide improved classification accuracy over the original Nefclass classifier when trained on skewed data. The proposed model uses two alternative discretization methods, MME and CAIM, to initialize fuzzy sets. From this study it is found that using the MME and CAIM 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.
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The authors gratefully acknowledge the support of NSERC, the National Sciences and Engineering Research Council of Canada, for ongoing grant support.
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Yousefi, J., Hamilton-Wright, A. (2019). Input Value Skewness and Class Label Confusion in the NEFCLASS Neuro-Fuzzy System. In: Merelo, J.J., et al. Computational Intelligence. IJCCI 2016. Studies in Computational Intelligence, vol 792. Springer, Cham. https://doi.org/10.1007/978-3-319-99283-9_9
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DOI: https://doi.org/10.1007/978-3-319-99283-9_9
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