Is an ordinal class structure useful in classifier learning?
type: Item
instance of: scholarly article
title: Is an ordinal class structure useful in classifier learning?
zbMATH Open document ID: 1184.68402
DOI: 10.1504/IJDMMM.2008.022537
author: Jens C. Hühn
author: Eyke Hüllermeier
published in: International Journal of Data Mining, Modelling and Management (IJDMMM)
publication date: 2009-12-22T00:00:00Z
review text: Summary: In recent years, a number of machine learning algorithms have been developed for the problem of ordinal classification. These algorithms try to exploit, in one way or the other, the order information of the problem, essentially relying on the assumption that the ordinal structure of the set of class labels is also reflected in the topology of the instance space. The purpose of this paper is to investigate, on an experimental basis, the validity of this assumption. Moreover, we seek to answer the question to what extent existing techniques and learning algorithms for ordinal classification are able to exploit order information and which properties of these techniques are important in this regard.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 68W05
Mathematics Subject Classification ID: 68T10
zbMATH DE Number: 5651375
zbMATH Keywords: ordinal classification
zbMATH Keywords: binary decomposition
zbMATH Keywords: nested dichotomies
zbMATH Keywords: pairwise classification
describes a project that uses: UCI-ml
MaRDI profile type: Publication
full work available at URL: ijdmmm.2008.022537
OpenAlex ID: W2158443418
description: scientific article; zbMATH DE number 5651375