D-semble: Efficient Diversity-Guided Search for Resilient ML Ensembles
Bibliographic record
Abstract
Supervised Machine Learning (ML) is used in many safety-critical applications, such as self-driving cars and medical imaging. Unfortunately, many training datasets have been discovered to contain faults. The accuracy of individual models when trained with faulty datasets can significantly degrade. In comparison, ensembles, consisting of multiple models combined through simple majority voting, are able to retain accuracy despite training data faults, due to their classification diversity, and are thus more resilient. However, there are many different ways to generate ML ensembles, and their accuracy can significantly differ. This creates a large search space for ensembles, making it challenging to find ensembles that maximize accuracy despite training data faults. We identify three different ways to generate diverse ML models, and present D-semble, a technique that uses Genetic Algorithms and diversity to efficiently search for resilient ensembles. We evaluate D-semble by measuring the balanced accuracies and F1-scores of ensembles it finds. Compared with bagging, greedy search, random selection, and the best individual model, ensembles found by D-semble are on average 9%, 16%, 28%, 32% more resilient respectively.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".