Creator:

Simiński, Krzysztof

Contributor:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Title:

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

Group publication title:

AMCS, volume 31 (2021)

Subject and Keywords:

outliers ; neuro-fuzzy systems ; clustering ; classification ; regression

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2021

Resource Type:

artykuł

DOI:

10.34768/amcs-2021-0021

Pages:

303-319

Source:

AMCS, volume 31, number 2 (2021) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego