Advances in knowledge discovery and data mining
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Empirical Software Engineering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Evaluating learning algorithms and classifiers
International Journal of Intelligent Information and Database Systems
A Collaborative Decision Support Platform for Product Release Definition
ICIW '10 Proceedings of the 2010 Fifth International Conference on Internet and Web Applications and Services
Learning to detect spyware using end user license agreements
Knowledge and Information Systems
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
Special issue on repeatable results in software engineering prediction
Empirical Software Engineering
Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment
Information Sciences: an International Journal
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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.