Visual-based analysis of classification measures and their properties for class imbalanced problems

With a plethora of available classification performance measures, choosing the right metric for the right task requires careful thought. To make this decision in an informed manner, one should study and compare general properties of candidate measures. However, analysing measures with respect to complete ranges of their domain values is a difficult and challenging task. In this study, we attempt to support such analyses with a specialized visualisation technique, which operates in a barycentric coordinate system using a 3D tetrahedron. Additionally, we adapt this technique to the context of imbalanced data and put forward a set of measure properties, which should be taken into account when examining a classification performance measure. As a result, we compare 22 popular measures and show important differences in their behaviour. Moreover, for parametric measures such as the F-beta and IBA(alpha)(G-mean), we analytically derive parameter thresholds that pinpoint the changes in measure properties. Finally, we provide an online visualisation tool that can aid the analysis of measure variability throughout their entire domains. (C) 2018 Elsevier Inc. All rights reserved.