Soft computing methods applied to combination of one-class classifiers

doi:10.1016/j.neucom.2011.02.023

2012-01-01

PRE

Tomasz Wilk

Michał Woźniak *

DOI：10.1016/j.neucom.2011.02.023

原文链接

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摘要

En 中文

The paper shows the possibilities of generalizing the two-class classification into multi-class classification by means of a fuzzy inference system. Fuzzy combiner harnesses the support values from classifiers to provide final response having no other restrictions on their structure. We compare proposed combination methods with ECOC and two variations of decision templates, based on Euclidean and symmetric distance. The effectiveness of the proposed combination method based on the fuzzy logic theory is also evaluated via computer experiments carried out on benchmark datasets. (C) 2011 Elsevier B.V. All rights reserved.

Keyword:

Fuzzy inference system

ANFIS

One-class classifiers

Combined classifiers

ECOC

Decision templates

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期刊

Neurocomputing

IF:

6.5

论文数:

2.5W

被引数:

6.5W

机构

wroclaw university of science & technology

学者数:

7.4K

论文数: 7.1K

被引数: 2

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