Summary of Suds: a Strategy For Unsupervised Drift Sampling, by Christofer Fellicious et al.
SUDS: A Strategy for Unsupervised Drift Sampling
by Christofer Fellicious, Lorenz Wendlinger, Mario Gancarski, Jelena Mitrovic, Michael Granitzer
First submitted to arxiv on: 5 Nov 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 A novel approach to tackling concept drift in machine learning models, known as Strategy for Drift Sampling (SUDS), is proposed. This method selects homogeneous samples for retraining using existing drift detection algorithms, enhancing model adaptability to evolving data. SUDS seamlessly integrates with current drift detection techniques. Additionally, the Harmonized Annotated Data Accuracy Metric (HADAM) is introduced, evaluating classifier performance in relation to the quantity of annotated data required, taking into account the difficulty of acquiring labeled data. The contributions are twofold: SUDS combines drift detection with strategic sampling to improve retraining, and HADAM balances classifier performance with labeled data usage. Empirical results demonstrate the efficacy of SUDS in optimizing labeled data use in dynamic environments, significantly improving machine learning application performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models often struggle with concept drift, where data distribution changes over time, affecting model performance. A new method called Strategy for Drift Sampling (SUDS) helps by choosing the right samples to retrain the model after a shift occurs. SUDS works well with existing methods that detect when a change has happened. Another innovation is the Harmonized Annotated Data Accuracy Metric (HADAM), which measures how good a classifier is and how much labeled data it needs to achieve that level of performance. This helps us use our resources more efficiently. |
Keywords
* Artificial intelligence * Machine learning
