Summary of A Comparative Study Of Sampling Methods with Cross-validation in the Fedhome Framework, by Arash Ahmadi et al.
A Comparative Study of Sampling Methods with Cross-Validation in the FedHome Framework
by Arash Ahmadi, Sarah S. Sharif, Yaser M. Banad
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 This paper compares various oversampling techniques in the context of federated learning for personalized in-home health monitoring. The researchers evaluate six methods, including SMOTE, Borderline-SMOTE, Random OverSampler, SMOTE-Tomek, SVM-SMOTE, and SMOTE-ENN, on FedHome’s public implementation using stratified K-fold cross-validation. The results show that SMOTE-ENN achieves the most consistent test accuracy, with a standard deviation range of 0.0167-0.0176, making it a reliable choice for enhancing the reliability and accuracy of personalized health monitoring systems within the FedHome framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares different ways to balance out uneven data in healthcare monitoring. They tested six methods to see which one works best with a special kind of learning that helps keep personal information private. The results show that one method, called SMOTE-ENN, is the most consistent and reliable for making sure health monitors work well. |
Keywords
» Artificial intelligence » Federated learning
