Sets of Receiver Operating Characteristic Curves and their Use in the Evaluation of Multi-Class Classification

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

5 Citations (Scopus)

Abstract

Within the last two decades, Receiver Operating Characteristic (ROC) Curves have become a standard tool for the analysis and comparison of classifiers since they provide a convenient graphical display of the trade-off between true and false positive classification rates for two class problems. However, there has been relatively little work examining ROC for more than two classes. Here we present an extension of ROC curves which can be used for illustrating and analyzing the quality of multi-class classifiers. Instead of using one single curve, we deal with sets of curves which are calculated for each class separately. These are used for analyzing not only how exactly the classes are separated, but also how clearly the classifier is able to distinguish the given classes. Apart from making it possible to analyze the results graphically, several values describing the classifier's quality can be calculated.

Original languageEnglish
Title of host publicationGECCO 2006 - Genetic and Evolutionary Computation Conference
PublisherACM Sigevo
Pages1601-1602
Number of pages2
ISBN (Print)1595931864, 9781595931863
DOIs
Publication statusPublished - 2006
EventGenetic and Evolutionary Computation Conference GECCO 2006 - Seattle, United States
Duration: 8 Jul 200612 Jul 2006

Publication series

NameGECCO 2006 - Genetic and Evolutionary Computation Conference
Volume2

Conference

ConferenceGenetic and Evolutionary Computation Conference GECCO 2006
CountryUnited States
CitySeattle
Period08.07.200612.07.2006

Keywords

  • Classifier Systems
  • Data Mining
  • Machine Learning
  • Pattern Recognition and Classification

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