An outlier-robust neuro-fuzzy system for classification and regression

• Autorzy: Siminski Krzysztof

Identyfikatory

DOI

10.34768/amcs-2021-0021

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

outliers; neuro-fuzzy system; clustering algorithm; regression

PL

wyjątki; system neurorozmyty; algorytm grupowania

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2021
• Tom: Vol. 31, no. 2
• Strony: 303--319
• Opis fizyczny: Bibliogr. 75 poz., tab., wykr.

Twórcy

autor

Siminski Krzysztof

• Department of Algorithmics and Software, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).