Visual-Based Analysis of Classification Measures with Applications to Imbalanced Data
نویسندگان
چکیده
With a plethora of available classification performance measures, choosing the right metric for the right task requires careful thought. However, analyzing measures with respect to complete ranges of their 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 21 popular measures and show important differences in their behavior. Finally, we provide an online visualization tool that can aid the analysis of complete ranges of performance measures.
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عنوان ژورنال:
- CoRR
دوره abs/1704.07122 شماره
صفحات -
تاریخ انتشار 2017