1. bookVolume 31 (2021): Issue 2 (June 2021)
Journal Details
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First Published
05 Apr 2007
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4 times per year
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English
access type Open Access

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

Published Online: 08 Jul 2021
Page range: 303 - 319
Received: 09 Nov 2020
Accepted: 09 Feb 2021
Journal Details
License
Format
Journal
First Published
05 Apr 2007
Publication timeframe
4 times per year
Languages
English
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

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