Maximizing upgrading and downgrading margins for ordinal regression
type: Item
instance of: scholarly article
title: Maximizing upgrading and downgrading margins for ordinal regression
zbMATH Open document ID: 1263.49033
author: Emilio Carrizosa
author: Belen Martin-Barragan
published in: Mathematical Methods of Operations Research
publication date: 2013-02-20T00:00:00Z
full work available at URL: Maximizing Upgrading and Downgrading Margins for Ordinal.pdf
review text: The paper is a contribution to the theory of classifying a set of objects into a set of ranked classes. Classifying an object in a class with a higher rank than its actual rank is called upgrading error. Similarly classifying an object in a class of lower rank is called downgrading error. Upgrading and downgrading errors are considered as two different classifier quality criteria to be taken into account. A support vector machines approach to maximization of undergrading and downgrading margins as an effective method of minimizing classification errors is proposed. A biobjective optimization problem is used to maximize simultaneously the upgrading and downgrading margins. It is shown that the resulting set of Pareto-optimal solutions can be obtained by solving one quadratic optimization problem. Several illustrative examples in the concluding part of the paper help to understand the proposed approach and applicability of the results.
reviewed by: Karel Zimmermann
Mathematics Subject Classification ID: 49M37
Mathematics Subject Classification ID: 90C29
zbMATH DE Number: 6137586
zbMATH Keywords: multi-objective optimization
zbMATH Keywords: support vector machines
zbMATH Keywords: ordinal regression
describes a project that uses: HandTill2001
MaRDI profile type: Publication
OpenAlex ID: W2112180281
cites work: Reducing multiclass to binary: A unifying approach for margin classifiers
cites work: Multicategory classification by support vector machines
cites work: Deriving weights in multiple-criteria decision making with support vector machines
cites work: Two-group classification via a biobjective margin maximization model
cites work: Multi-group support vector machines with measurement costs: A biobjective approach
cites work: Support Vector Ordinal Regression
cites work: Support-vector networks
cites work: An introduction to support vector machines and other kernel-based learning methods.
cites work: Learning Rule Ensembles for Ordinal Classification with Monotonicity Constraints
cites work: Stochastic dominance-based rough set model for ordinal classification
cites work: Combining discriminant models with new multi-class SVMs
cites work: A simple generalisation of the area under the ROC curve for multiple class classification problems
cites work: Classification by pairwise coupling
cites work: A PAC-Bayesian margin bound for linear classifiers
cites work: Evolutionary Multi-Criterion Optimization
cites work: A confidence voting process for ranking problems based on support vector machines
cites work: A review of ordinal regression models applied on health-related quality of life assessments
cites work: Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
cites work: Linear and Nonlinear Separation of Patterns by Linear Programming
cites work: MOP/GP models for machine learning
cites work: Support vector machines with different norms: Motivation, formulations and results
cites work: Asymmetric distances, semidirected networks and majority in Fermat-Weber problems
cites work: Q4856771
cites work: Q4261789
cites work: On the scalability of ordered multi-class ROC analysis
DOI: 10.1007/S00186-011-0368-Z
description: scientific article; zbMATH DE number 6137586