Mining Evolving Data Streams with Particle Filters
Bibliographic record
Abstract
We propose a particle filter‐based learning method, PF‐LR, for learning logistic regression models from evolving data streams. The method inherently handles concept drifts in a data stream and is able to learn an ensemble of logistic regression models with particle filtering. A key feature of PF‐LR is that in its resampling, step particles are sampled from the ones that maximize the classification accuracy on the current data batch. Our experiments show that PF‐LR gives good performance, even with relatively small batch sizes. It reacts to concept drifts quicker than conventional particle filters while being robust to noise. In addition, PF‐LR learns more accurate models and is more computationally efficient than the gradient descent method for learning logistic regression models. Furthermore, we evaluate PF‐LR on both synthetic and real data sets and find that PF‐LR outperforms some other state‐of‐the‐art streaming mining algorithms on most of the data sets tested.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".