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Towards Application-specific Evaluation Metrics
Responsible organisation
2008 (English)Conference paper, (Refereed) Published
Abstract [en]

Classifier evaluation has historically been conducted by estimating predictive accuracy via cross-validation tests or similar methods. More recently, ROC analysis has been shown to be a good alternative. However, the characteristics vary greatly between problem domains and it has been shown that some evaluation metrics are more appropriate than others in certain cases. We argue that different problems have different requirements and should therefore make use of evaluation metrics that correspond to the relevant requirements. For this purpose, we motivate the need for generic multi-criteria evaluation methods, i.e., methods that dictate how to integrate metrics but not which metrics to integrate. We present such a generic evaluation method and discuss how to select metrics on the basis of the application at hand.

Place, publisher, year, edition, pages
Helsinki, 2008.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-8553Local ID: oai:bth.se:forskinfoF9C495DF02D8E52FC125748A0057CE57OAI: oai:DiVA.org:bth-8553DiVA: diva2:836279
Conference
The 3rd workshop on Evaluation Methods for Machine Learning
Available from: 2012-09-18 Created: 2008-07-18 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(50 kB)21 downloads
File information
File name FULLTEXT01.pdfFile size 50 kBChecksum SHA-512
09e2dfebade32f6a6f94e6dda06f311fe97d8c181345a7f337284d891ca6d667cc16bf776741544983f2b2b9d3bb81063f856385303029dde32d81b3656ce60b
Type fulltextMimetype application/pdf

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Lavesson, Niklas
Computer Science

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf