Relevance as a metric for evaluating machine learning algorithms

A. Kota Gopalakrishna, T. Ozcelebi, A. Liotta, J.J. Lukkien

Research output: Book/ReportReportAcademic

9 Citations (Scopus)
322 Downloads (Pure)


In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel probability-based performance metric called Relevance Score for evaluating supervised learning algorithms. We evaluate the proposed metric through empirical analysis on a dataset gathered from an intelligent lighting pilot installation. In comparison to the commonly used Classification Accuracy metric, the Relevance Score proves to be more appropriate for a certain class of applications. Keywords: Machine learning algorithms; performance metric; probabilistic approach
Original languageEnglish
Place of PublicationEindhoven
PublisherTechnische Universiteit Eindhoven
Number of pages14
Publication statusPublished - 2013

Publication series

NameComputer science reports
ISSN (Print)0926-4515


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