Standardizing the comparison of partitions
type: Item
instance of: scholarly article
title: Standardizing the comparison of partitions
zbMATH Open document ID: 1037.62055
author: Ursula Garczarek
author: Claus Weihs
published in: Computational Statistics
publication date: 2004-03-16T00:00:00Z
full work available at URL: http://hdl.handle.net/2003/5274
review text: A class of \(\arg\max\) classification rules is considered in which a membership function \(m(x,c)\) is attached to each class \(c=1,\dots,G\). An object with predictor value \(x\) is classified to the class \(\hbox{cl}(x)=\arg\max_{c=1,\dots,G} m(x,c)\). (In Bayes classification \(m(x,c)\) are the posterior probabilities to belong to the class \(c\)). The authors propose a standardizing technique for \(m(x,c)\) which allows to compare different \(\arg\max\) classifiers. This technique captures such features of the classifier as non-resemblance (the quality of membership values separation in different classes). Vizualization of the obtained partitions for three (or four) classes is discussed. Results are presented for classification of simulated chi-square distributed r.v.s and for Fisher's Iris data.
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 62A09
zbMATH DE Number: 2058965
zbMATH Keywords: classifier
zbMATH Keywords: accuracy
zbMATH Keywords: non-resemblance
zbMATH Keywords: visualization
describes a project that uses: BayesDA
describes a project that uses: R
describes a project that uses: UCI-ml
describes a project that uses: Neural Network Toolbox
MaRDI profile type: Publication
OpenAlex ID: W1556027204
cites work: Q3255781
cites work: Q4485852
DOI: 10.1007/S001800300136
description: scientific article; zbMATH DE number 2058965