Summary of The More the Merrier? Navigating Accuracy Vs. Energy Efficiency Design Trade-offs in Ensemble Learning Systems, by Rafiullah Omar et al.
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems
by Rafiullah Omar, Justus Bogner, Henry Muccini, Patricia Lago, Silverio Martínez-Fernández, Xavier Franch
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE)
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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 Medium Difficulty Summary: This paper investigates machine learning model composition for energy-efficient ensemble learning. The study analyzes three design decisions: ensemble size, fusion methods, and partitioning methods. By combining four popular classification algorithms in different ensembles, the authors conducted a full factorial experiment with 176 combinations. Results show that increasing ensemble size does not significantly improve accuracy but increases energy consumption. Majority voting outperformed meta-model fusion in terms of both accuracy and energy efficiency. Subset-based training led to lower energy consumption while whole-dataset training did not increase accuracy. The paper concludes that designing ensembles with small sizes, subset-based training, majority voting, and energy-efficient algorithms like decision trees or Naive Bayes is recommended from a Green AI perspective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This study looks at how to make computer programs learn better together without using too much energy. The researchers tested different ways of combining four types of machine learning models into teams (ensembles). They found that making the team smaller and using simple voting instead of complicated calculations can help both with accuracy and energy efficiency. Using only part of the data to train the models also uses less energy. Overall, the study suggests that small teams with simple voting and efficient algorithms are a good way to make machine learning work better while saving energy. |
Keywords
» Artificial intelligence » Classification » Machine learning » Naive bayes
