Name
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. 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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Other Computer Science | Data mining,Visualization,Computer science,Artificial intelligence,Online visualization,Tetrahedron,Machine learning,Barycentric coordinate system |
DocType | Volume | Citations |
Journal | abs/1704.07122 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dariusz Brzezinski | 1 | 213 | 11.28 |
| Jerzy Stefanowski | 2 | 1653 | 139.25 |
| Robert Susmaga | 3 | 370 | 33.32 |
| Izabela Szczęch | 4 | 56 | 7.90 |