TY - JOUR
T1 - A comprehensive survey of error measures for evaluating binary decision making in data science
AU - Emmert-Streib, Frank
AU - Moutari, Salissou
AU - Dehmer, Matthias
N1 - Funding Information:
M.D. thanks the Austrian Science Funds for supporting this work (project P30031).
Publisher Copyright:
© 2019 The Authors. WIREs Data Mining and Knowledge Discovery published by Wiley Periodicals, Inc.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2019/9
Y1 - 2019/9
N2 - 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.
AB - 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.
KW - classification
KW - data science
KW - decision making
KW - error measures
KW - machine learning
KW - statistics
UR - http://www.scopus.com/inward/record.url?scp=85061296075&partnerID=8YFLogxK
U2 - 10.1002/widm.1303
DO - 10.1002/widm.1303
M3 - Review article
C2 - 31656552
SN - 1942-4795
VL - 9
SP - e1303
JO - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
JF - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
IS - 5
M1 - e1303
ER -