Title: Visual-Based Analysis of Classification Measures with Applications to Imbalanced Data,
Abstract: 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 visualization
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 properties which should be taken into account
when selecting 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 change measure properties.
Finally, we provide an online visualization tool that can aid the analysis of
complete domain ranges of performance measures.