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Paper details
Number 2 - June 2021
Volume 31 - 2021
An outlier-robust neuro-fuzzy system for classification and regression
Krzysztof Siminski
Abstract
Real life data often suffer from non-informative objects—outliers. These are objects that are not typical in a dataset and
can significantly decline the efficacy of fuzzy models. In the paper we analyse neuro-fuzzy systems robust to outliers in
classification and regression tasks. We use the fuzzy c-ordered means (FCOM) clustering algorithm for scatter domain
partition to identify premises of fuzzy rules. The clustering algorithm elaborates typicality of each object. Data items with
low typicalities are removed from further analysis. The paper is accompanied by experiments that show the efficacy of our
modified neuro-fuzzy system to identify fuzzy models robust to high ratios of outliers.
Keywords
outliers, neuro-fuzzy systems, clustering, classification, regression