The PerfSim Algorithm for Concept Drift Detection in Imbalanced Data
Bibliographic record
Abstract
There is currently a surge of interest in adaptive learning algorithms for applications ranging from ozone level peak predictions, learning stock market indicators, and detecting smart phone usage patterns. In such scenarios, the detection of change (or drift) in the concept being learned is important to ensure that correct, timely and relevant models are constructed. In addition, such data is often imbalanced and, to further complicate the issue, we are frequently interested in learning the minority class. It follows that ignoring these two aspects during learning may lead to unreliable, or even incorrect, models being built. In this research we discuss the interplay between concept drift detection and imbalanced data sets in order to ensure reliable results. We introduce a novel algorithm that, rather than considering a single performance evaluation measure such as accuracy for change detection, considers all the components of a confusion matrix and employs the cosine similarity coefficient. We evaluate our algorithm against a real world mobile phone database, as well as benchmarking datasets, and we compare it with two other state-of-the-art methods. The results show that our approach is particularly sensitive to concept drifts occurring in imbalanced data sets. Our evaluation indicates that our algorithm is able to detect concept drift reliably. Further, our method is shown to perform very well compared to the other techniques, especially when the drift occurs in the minority class of a class imbalance problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".