Summary of Aigas-devl: An Adaptive Incremental Neural Gas Model For Drifting Data Streams Under Extreme Verification Latency, by Maria Arostegi et al.
AiGAS-dEVL: An Adaptive Incremental Neural Gas Model for Drifting Data Streams under Extreme Verification Latency
by Maria Arostegi, Miren Nekane Bilbao, Jesus L. Lobo, Javier Del Ser
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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, AiGAS-dEVL (Adaptive Incremental neural GAS model for drifting Streams under Extreme Verification Latency), is proposed to address the challenge of adapting Machine Learning models to concept drifts in partially labeled data streams with extreme verification latency. This approach relies on growing neural gas to characterize distributions of concepts over time, enabling online analysis and adaptation policies. AiGAS-dEVL is evaluated on several synthetic datasets, outperforming state-of-the-art baselines in terms of adaptability while ensuring interpretable instance-based strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models can struggle when data is partially labeled or streaming setups are affected by concept drifts. This paper proposes a new approach called AiGAS-dEVL to help these models adapt. It uses a growing neural gas model to track changes in the data over time and make adjustments accordingly. The results show that this approach works well on different datasets and is better than other methods at adapting to changing patterns. |
Keywords
» Artificial intelligence » Machine learning
