<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"An outlier\u2013robust neuro\u2013fuzzy system for classification and regression"}

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...

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

Data publikacji: 08 lip 2021

Zakres stron: 303 - 319

Otrzymano: 09 lis 2020

Przyjęty: 09 lut 2021

DOI: https://doi.org/10.34768/amcs-2021-0021

Słowa kluczowe
outliers, neuro-fuzzy systems, clustering, classification, regression

© 2021 Krzysztof Siminski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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

Data publikacji: 08 lip 2021

Zakres stron: 303 - 319

Otrzymano: 09 lis 2020

Przyjęty: 09 lut 2021

DOI: https://doi.org/10.34768/amcs-2021-0021

Słowa kluczoweoutliers, neuro-fuzzy systems, clustering, classification, regression

© 2021 Krzysztof Siminski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
outliers, neuro-fuzzy systems, clustering, classification, regression