Summary of A Probabilistic Framework For Adapting to Changing and Recurring Concepts in Data Streams, by Ben Halstead et al.
A Probabilistic Framework for Adapting to Changing and Recurring Concepts in Data Streams
by Ben Halstead, Yun Sing Koh, Patricia Riddle, Mykola Pechenizkiy, Albert Bifet
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 probabilistic method, SELeCT, enables learning in changing conditions by continuously evaluating the relevance of past experience. It maintains distinct internal states for each concept, representing relevant experience with a unique classifier. The algorithm estimates state relevance using a Bayesian approach that combines the likelihood of recent observations and a transition pattern prior based on the system’s current state. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach is needed to handle changing conditions in streaming data, where only a subset of previous experience is relevant. SELeCT can learn from recurring concepts by maintaining separate internal states for each concept and evaluating their relevance over time. This method outperforms existing approaches that do not consider experience relevance or only evaluate it sparsely. |
Keywords
» Artificial intelligence » Likelihood
