Learning event count models with application to affiliation ranking
Bibliographic record
Abstract
Event count prediction is a class of problems in time series analysis, which has been extensively studied over the years. Its applications range from the prediction of the number of publications in the scientific community to ATM cash withdrawal transaction prediction in the banking industry. However, in applied data science problems, using event count prediction models for real-world data often faces difficulties because the data violates not only the Poisson distribution assumption, i.e., the rate at which events occur should be constant, but the data is also relatively sparse, i.e., only a few event count values are greater than zero. Traditional techniques do not work well under these two conditions. To overcome these limitations, some researchers have proposed the generic autoregressive (AR) models for event count prediction, which work with non-constant event occurrence rates. As AR models solely use historical event count for forecasting, they might not be as flexible for incorporating domain knowledge. Moreover, and similarly, AR models may not work very well with the relatively short length-time series. In order to overcome these challenges, we propose a machine learning approach to address the event count prediction problem. We benchmark our proposed solution on the KDD Cup 2016 dataset by formalizing affiliation ranking as an event count time series prediction problem. We map the time series onto a highly dimensional state space and systematically apply the state-of-the-art machine learning algorithms to predict event counts. We then compare our proposed approach against solutions in the KDD Cup 2016 competition and show that our work outperforms the best models in this with an [email protected] score of 0.7573.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".