Summary of Balancing the Scales: a Comprehensive Study on Tackling Class Imbalance in Binary Classification, by Mohamed Abdelhamid and Abhyuday Desai
Balancing the Scales: A Comprehensive Study on Tackling Class Imbalance in Binary Classification
by Mohamed Abdelhamid, Abhyuday Desai
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 The paper comprehensively evaluates three widely-used strategies for handling class imbalance in binary classification tasks: SMOTE, Class Weights tuning, and Decision Threshold Calibration. The study compares these methods against a baseline scenario of no-intervention across 15 diverse machine learning models and 30 datasets from various domains, conducting a total of 9,000 experiments. Performance is assessed using the F1-score, as well as nine additional metrics including F2-score, precision, recall, Brier-score, PR-AUC, and AUC. The results show that all three strategies generally outperform the baseline, with Decision Threshold Calibration emerging as the most consistently effective technique. However, variability in the best-performing method across datasets highlights the importance of testing multiple approaches for specific problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to fix a common problem in machine learning called class imbalance. This happens when one group has way more data than another, which can make it hard for models to learn well. The researchers tested three different methods to see if they could improve the situation: SMOTE, Class Weights, and Decision Threshold Calibration. They tried these methods with 15 different machine learning models on 30 different datasets from different areas like text, images, or sounds. They used nine different ways to measure how well each method worked. The results showed that all three methods did better than not doing anything at all. One method, called Decision Threshold Calibration, was the best most of the time. But it’s also important to try out different methods on different datasets because what works best can depend on the specific problem. |
Keywords
» Artificial intelligence » Auc » Classification » F1 score » Machine learning » Precision » Recall
