A context-aware machine learning-based approach
Bibliographic record
Abstract
It is known that training a general and versatile Machine Learning (ML)-based model is more cost-effective than training several specialized ML-models for different operating contexts. However, as the volume of training information grows, the higher the probability of producing biased results. Learning bias is a critical problem for many applications, such as those related to healthcare scenarios, environmental monitoring and air traffic control. In this paper, we compare the use of a general model that was trained using all contexts against a system that is composed of a set of specialized models that was trained for each particular operating context. For this purpose, we propose a local learning approach based on context-awareness, which involves: (i) anticipating, analyzing and representing context changes; (ii) training and finding machine learning models to maximize a given scoring function for each operating context; (iii) storing trained ML-based models and associating them with corresponding operating contexts; and (iv) deploying a system that is able to select the best-fit ML-based model at runtime based on the context. To illustrate our proposed approach, we reproduce two experiments: one that uses a neural network regression-based model to perform predictions and another one that uses an evolutionary neural network-based approach to make decisions. For each application, we compare the results of the general model, which was trained based on all contexts, against the results of our proposed approach. We show that our context-aware approach can improve results by alleviating bias with different ML tasks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".