AN OUTLIER-ROBUST NEURO-FUZZY SYSTEM FOR CLASSIFICATION AND REGRESSION

被引：9

作者：

Siminski, Krzysztof ^{[1
]}

机构：

[1] Silesian Tech Univ, Dept Algorithm & Software, Ul Akad 16, PL-44100 Gliwice, Poland

来源：

INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE | 2021年 / 31卷 / 02期

关键词：

outliers; neuro-fuzzy systems; clustering; classification; regression; IDENTIFICATION; KERNEL; NETWORK; MODELS;

D O I：

10.34768/amcs-2021-0021

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

下载

页码：303 / 319

页数：17

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