Pattern classification with principal component analysis and fuzzy rule bases
type: Item
instance of: scholarly article
title: Pattern classification with principal component analysis and fuzzy rule bases
zbMATH Open document ID: 0986.90015
DOI: 10.1016/S0377-2217(99)00307-0
published in: European Journal of Operational Research
publication date: 2001-03-28T00:00:00Z
review text: The authors propose a way of reducing the feature space dimension, via the principal components analysis. They show that comparison of the present study with their earlier one [\textit{V. Ravi} and \textit{H.-J. Zimmermann}, ibid. 123, 16-28 (2000; Zbl 0961.90135)] is simply not meaningful because the principal components are only linear combinations of the original feature variables and hence, they do not reflect of the importance or otherwise of the original variables.
reviewed by: Vl. D. Mazurov
Mathematics Subject Classification ID: 90B50
zbMATH DE Number: 1582676
zbMATH Keywords: pattern classification
zbMATH Keywords: fuzzy set
zbMATH Keywords: principal component analysis
MaRDI profile type: Publication
cites work: Q4023089
cites work: Q3348736
cites work: Fuzzy rule based classification with FeatureSelector and modified threshold accepting
cites work: Q3994588
cites work: Fuzzy sets
cites work: Latent connectives in human decision making
cites work: Q3997786
full work available at URL: s0377-2217(99)00307-0
OpenAlex ID: W2134034311
author: V. Ravi
author: P. J. Reddy
author: Hans-Jürgen Zimmermann
description: scientific article; zbMATH DE number 1582676