Summary of Learning with Confidence: Training Better Classifiers From Soft Labels, by Sjoerd De Vries and Dirk Thierens
Learning with Confidence: Training Better Classifiers from Soft Labels
by Sjoerd de Vries, Dirk Thierens
First submitted to arxiv on: 24 Sep 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 paper explores the idea of incorporating label uncertainty into machine learning models. Traditional approaches use data with definite class labels, but this neglects the inherent uncertainty in these labels. The authors investigate whether using soft labels, which represent discrete probability distributions over class labels, can improve predictive performance. They first demonstrate the potential value of soft label learning (SLL) for estimating model parameters in a simulation experiment, particularly for limited sample sizes and imbalanced data. Then, they compare the performance of various wrapper methods for learning from both hard and soft labels using identical base classifiers. The results show that SLL methods consistently outperform hard label methods on real-world-inspired synthetic data with clean labels. However, when dealing with noisy probability estimates, the authors study the effect of miscalibration on model performance. They find that SLL methods outperform hard label methods in most settings. Finally, they evaluate the methods on a real-world dataset with confidence scores, where SLL methods match traditional methods for predicting (noisy) hard labels while providing more accurate confidence estimates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using soft labels in machine learning to make predictions better. Soft labels are like probabilities that say how likely something is to be in one class or another. The authors test different ways of using soft labels and find that they work better than traditional methods on some types of data. They also look at what happens when the soft labels are not perfect, which is often the case with real-world data. Overall, the paper shows that using soft labels can be a useful way to improve machine learning models. |
Keywords
» Artificial intelligence » Machine learning » Probability » Synthetic data
