Automated Software Engineering

, Volume 21, Issue 1, pp 41–63 | Cite as

A method for evaluation of learning components

  • Niklas Lavesson
  • Veselka Boeva
  • Elena Tsiporkova
  • Paul Davidsson
Article

Abstract

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.

Keywords

Data mining Evaluation Machine learning 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Niklas Lavesson
    • 1
  • Veselka Boeva
    • 2
  • Elena Tsiporkova
    • 3
  • Paul Davidsson
    • 4
  1. 1.Blekinge Institute of TechnologyKarlskronaSweden
  2. 2.Computer Systems and Technologies DepartmentTechnical University of Sofia, branch PlovdivPlovdivBulgaria
  3. 3.Software Engineering and ICT GroupSirris, The Collective Center for the Belgian Technological IndustryBrusselsBelgium
  4. 4.Malmö UniversityMalmöSweden

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