A comprehensive survey of error measures for evaluating binary decision making in data science

Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
7 Downloads (Pure)

Abstract

Binary decision making is a topic of great interest for many fields, including biomedical science, economics, management, politics, medicine, natural science and social science, and much effort has been spent for developing novel computational methods to address problems arising in the aforementioned fields. However, in order to evaluate the effectiveness of any prediction method for binary decision making, the choice of the most appropriate error measures is of paramount importance. Due to the variety of error measures available, the evaluation process of binary decision making can be a complex task. The main objective of this study is to provide a comprehensive survey of error measures for evaluating the outcome of binary decision making applicable to many data-driven fields. This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Prediction Algorithmic Development > Statistics
Original languageEnglish
Article numbere1303
Number of pages15
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume9
Issue number5
Early online date08 Feb 2019
DOIs
Publication statusPublished - Sep 2019

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