Dynamic Ensemble Algorithm Post-Selection Using Hardness-Aware Oracle
Bibliographic record
Abstract
Dynamic Ensemble Selection (DES) algorithms have obtained better performance in many tasks compared to monolithic classifiers and static ensembles. However, it is reasonable to assume that no DES algorithm is the optimal solution in different scenarios since diversity plays an important role. Thus, this paper addresses this research gap by proposing a novel approach called Hardness-aware Oracle with Dynamic Ensemble Selection (HaO-DES) that operates as a post-selection strategy, evaluating and selecting the best DES techniques per instance. Each DES technique ensemble is evaluated using a new measure called Hardness-aware Oracle (HaO). HaO extends the traditional Oracle concept by assessing a DES technique based on how the classifiers in the selected ensemble work together, contrasting with the individual classifier evaluation in the traditional assessment. We performed experiments over 30 databases, using three base classifiers (Perceptron, Logistic Regression, and Naive Bayes) in homogeneous and heterogenous pools’ configurations, to assess HaO-DES with four DES approaches (KNORA-U, KNOP, DES-P, and META-DES). We use three performance metrics to evaluate the experiments: accuracy, F-score, and Matthews Correlation Coefficient (MCC). The results show that our approach outperforms or obtains similar results against the four individual DES approaches, mainly when considering heterogeneous pool settings. We also demonstrated the HaO-DES efficiency in choosing suitable DES techniques in different situations.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".