Summary of Ensloss: Stochastic Calibrated Loss Ensembles For Preventing Overfitting in Classification, by Ben Dai
EnsLoss: Stochastic Calibrated Loss Ensembles for Preventing Overfitting in Classification
by Ben Dai
First submitted to arxiv on: 2 Sep 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 ensemble method called EnsLoss is proposed, which combines loss functions within the empirical risk minimization (ERM) framework to optimize classification accuracy. This approach preserves the convexity and calibration properties of individual losses, enabling calibrated loss derivatives. The method uses doubly stochastic gradient descent with random batch samples and calibrated loss derivatives, similar to Dropout. Experimental results on 14 OpenML tabular datasets and 46 image datasets demonstrate the effectiveness of EnsLoss compared to fixed loss methods. The proposed approach is theoretically consistent and benefits from preserving the legitimacy of combined losses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EnsLoss is a new way to combine different loss functions in machine learning to get better results for classification tasks. It’s like taking multiple views of the same thing and combining them into one clear picture. This helps ensure that the combined loss functions are correct and give consistent results. The approach uses a special type of gradient descent, similar to a technique called Dropout, which helps improve performance. Tests show that EnsLoss works well on many different types of datasets. |
Keywords
» Artificial intelligence » Classification » Dropout » Gradient descent » Machine learning » Stochastic gradient descent
