Summary of Robust Performance Metrics For Imbalanced Classification Problems, by Hajo Holzmann and Bernhard Klar
Robust performance metrics for imbalanced classification problems
by Hajo Holzmann, Bernhard Klar
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Methodology (stat.ME)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel study reveals that traditional evaluation metrics for binary classification, such as F-score, Jaccard similarity coefficient, and Matthews’ correlation coefficient (MCC), lack robustness in class-imbalanced scenarios. As the minority class proportion approaches zero, these metrics favor classifiers that ignore the minority class. To address this issue, the authors propose modified versions of the F-score and MCC that maintain a non-zero true positive rate (TPR) even in strongly imbalanced settings. Numerical simulations and analysis on a credit default dataset illustrate the behavior of various performance metrics. The study also explores connections to receiver operating characteristic (ROC) and precision-recall curves, providing recommendations for their combination with performance metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists found that common ways to measure how well a computer program can identify one class over another don’t work well when there are many more instances of one class. They show that these measures tend to ignore the smaller class as it becomes smaller. To solve this problem, they developed new versions of these measures that don’t ignore the minority class even when it’s very small. They tested these new measures on a dataset about credit defaults and found that they provide a more accurate picture of how well a program can identify good and bad loans. |
Keywords
» Artificial intelligence » Classification » Precision » Recall
