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@article{Hand:2023:10.1007/s11634-021-00490-3,
author = {Hand, DJ and Anagnostopoulos, C},
doi = {10.1007/s11634-021-00490-3},
journal = {Advances in Data Analysis and Classification},
pages = {109--124},
title = {Notes on the H-measure of classifier performance},
url = {http://dx.doi.org/10.1007/s11634-021-00490-3},
volume = {17},
year = {2023}
}

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TY  - JOUR
AB  - The H-measure is a classifier performance measure which takes into account the context of application without requiring a rigid value of relative misclassification costs to be set. Since its introduction in 2009 it has become widely adopted. This paper answers various queries which users have raised since its introduction, including questions about its interpretation, the choice of a weighting function, whether it is strictly proper, its coherence, and relates the measure to other work.
AU  - Hand,DJ
AU  - Anagnostopoulos,C
DO  - 10.1007/s11634-021-00490-3
EP  - 124
PY  - 2023///
SN  - 1862-5347
SP  - 109
TI  - Notes on the H-measure of classifier performance
T2  - Advances in Data Analysis and Classification
UR  - http://dx.doi.org/10.1007/s11634-021-00490-3
UR  - https://link.springer.com/article/10.1007%2Fs11634-021-00490-3
UR  - http://hdl.handle.net/10044/1/93425
VL  - 17
ER  -

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