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Evaluating learning algorithms and classifiers
Department Software and Systems Engineering, School of Engineering, Blekinge Institute of Technology, Ronneby, Sweden.ORCID iD: 0000-0002-0535-1761
Department Software and Systems Engineering, School of Engineering, Blekinge Institute of Technology, Ronneby, Sweden.
2007 (English)In: International Journal of Intelligent Information and Database Systems, ISSN 1751-5858, E-ISSN 1751-5866, Vol. 1, no 1, p. 37-52Article in journal (Refereed) Published
Abstract [en]

We analyse 18 evaluation methods for learning algorithms and classifiers, and show how to categorise these methods with the help of an evaluation method taxonomy based on several criteria. We also define a formal framework that make it possible to describe all methods using the same terminology, and apply it in a review of the state-of-the-art in learning algorithm and classifier evaluation. The framework enables comparison and deeper understanding of evaluation methods from different fields of research. Moreover, we argue that the framework and taxonomy support the process of finding candidate evaluation methods for a particular problem.

Place, publisher, year, edition, pages
2007. Vol. 1, no 1, p. 37-52
Keywords [en]
classification; evaluation methods; taxonomy; supervised learning; learning algorithms; classifiers
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37949DOI: 10.1504/IJIIDS.2007.013284OAI: oai:DiVA.org:hj-37949DiVA, id: diva2:1159921
Available from: 2012-09-18 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

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Lavesson, Niklas

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