Summary of Smooth Ranking Svm Via Cutting-plane Method, by Erhan Can Ozcan et al.
Smooth Ranking SVM via Cutting-Plane Method
by Erhan Can Ozcan, Berk Görgülü, Mustafa G. Baydogan, Ioannis Ch. Paschalidis
First submitted to arxiv on: 25 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed prototype learning approach maximizes the Area Under the Curve (AUC) during training by leveraging the cutting-plane method, similar to Ranking SVM. This unconventional strategy prevents overfitting by iteratively introducing cutting planes and penalizing large jumps in model weights. The algorithm outperforms relevant competitors on 25 datasets among 73 binary classification benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to develop a new way to train machine learning models that work well even when one class has more data than others. Instead of trying to get the best accuracy, the approach focuses on getting a good score with the Area Under the Curve (AUC). The method uses an old technique called cutting-plane and adds penalties to prevent big changes in the model’s weights. This helps the model learn smoothly and avoid overfitting. In experiments, this new approach did better than other methods on 25 out of 73 datasets. |
Keywords
* Artificial intelligence * Auc * Classification * Machine learning * Overfitting
