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A method for evaluation of learning components
Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation.ORCID iD: 0000-0002-0535-1761
Technical University of Sofia, branch Plovdiv Computer Systems and Technologies Department, Plovdiv, Bulgaria.
Software Engineering and ICT group Sirris, The Collective Center for the Belgian Technological Industry Brussels, Belgium.
Malmö University, Malmö, Sweden.
2014 (English)In: Automated Software Engineering: An International Journal, ISSN 0928-8910, E-ISSN 1573-7535, Vol. 21, no 1, 41-63 p.Article in journal (Refereed) Published
Abstract [en]

Today, it is common to include machine learning components in software products. These components offer specific functionalities such as image recognition, time series analysis, and forecasting but may not satisfy the non-functional constraints of the software products. It is difficult to identify suitable learning algorithms for a particular task and software product because the non-functional requirements of the product affect algorithm suitability. A particular suitability evaluation may thus require the assessment of multiple criteria to analyse trade-offs between functional and non-functional requirements. For this purpose, we present a method for APPlication-Oriented Validation and Evaluation (APPrOVE). This method comprises four sequential steps that address the stated evaluation problem. The method provides a common ground for different stakeholders and enables a multi-expert and multi-criteria evaluation of machine learning algorithms prior to inclusion in software products. Essentially, the problem addressed in this article concerns how to choose the appropriate machine learning component for a particular software product.

Place, publisher, year, edition, pages
Springer , 2014. Vol. 21, no 1, 41-63 p.
Keyword [en]
Data mining, Evaluation, Machine learning
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:hj:diva-37943DOI: 10.1007/s10515-013-0123-1ISI: 000330975100003Local ID: oai:bth.se:forskinfo792D7BDAD181A4BEC1257B5F0034EE80OAI: oai:DiVA.org:hj-37943DiVA: diva2:1159931
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2017-11-24Bibliographically approved

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Lavesson, Niklas

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Citation style
  • apa
  • harvard1
  • ieee
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  • vancouver
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  • de-DE
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  • Other locale
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Output format
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