Summary of Synthetic Information Towards Maximum Posterior Ratio For Deep Learning on Imbalanced Data, by Hung Nguyen and Morris Chang
Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data
by Hung Nguyen, Morris Chang
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 technique for generating synthetic data aims to balance class-imbalanced data by prioritizing informative regions. Unlike random-based oversampling, this method identifies high-entropy samples and generates well-placed synthetic data to enhance machine learning algorithms’ accuracy and efficiency. The algorithm optimizes the class posterior ratio to maximize the probability of generating a synthetic sample in the correct region of its class. To maintain data topology, synthetic data are generated within each minority sample’s neighborhood. Experimental results on forty-one datasets demonstrate the superior performance of this technique. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how class-imbalanced data affects deep learning models and proposes a new way to balance it. The goal is to make machine learning more accurate and efficient by generating fake data that helps train models better. The new method looks at “high-entropy” samples, which are important for the model’s learning process. By generating synthetic data in the right places, this technique improves the performance of deep-learning models. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Probability * Synthetic data
