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A method for evaluation of learning components
Blekinge Institute of Technology, School of Computing.
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
URN: urn:nbn:se:bth-6721DOI: 10.1007/s10515-013-0123-1ISI: 000330975100003Local ID: diva2:834254
Available from: 2014-04-23 Created: 2013-05-02 Last updated: 2015-06-30Bibliographically approved

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Lavesson, Niklas
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