Proteus: A scalable, flexible and extensible multi-classifier framework
Bibliographic record
Abstract
Though the popularity and demand for machine learning infrastructures is soaring in this age of “big data”, general purpose configuration and deployment strategies are still in their infancy. This paper presents Proteus, a flexible and extensible framework allowing different machine learning algorithms to be introduced in a plug-and-play manner in order to be evaluated. Proteus enables domain experts to more easily compare, contrast, and even combine results from classifiers including Deep Learning, GLM, GBM, Naive Bayes, Random Forest, SVM and Linear Regression. Leveraging this design, it is easier to explore the possibility that a combination of multiple classifiers may be the best approach to guaranteeing high accuracy. A case study involving 6 months of mouse-movement data from 5 patients with a Clinical Dementia Rating (CDR) of 0 (control group) and 5 patients with a CDR of 0.5 (considered a high impairment level) identifies the costs and benefits of this engineering effort towards a scalable, flexible and extensible architecture for multi-classifier analysis.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".