A dynamic multiple classifier system using graph neural network for high dimensional overlapped data
Bibliographic record
Abstract
Dynamic selection techniques select a subset of the classifiers from a pool according to their perceived competence in labeling each given query instance in particular. To do so, most techniques rely on the locality assumption for the selection task, meaning that similar instances should share a set of adequate classifiers, so their competencies are usually estimated over a local region surrounding the query. However, as the local distribution is crucial to these techniques, a poor region definition due to the presence of high dimensionality and class overlap can have a negative impact on their performance, thus limiting their application. Thus, we propose in this work a dynamic selection technique to better deal with sparse and overlapped data in which the instance–instance and the classifier–classifier relationships are leveraged to learn the dynamic classifier combination rule. The proposed technique uses a multi-label graph neural network as a meta-learner, so both the data modeled as a graph, without directly defining the local region, and the classifiers’ inter-dependencies modeled in the meta-labels are used to learn an embedded space where the dynamic selection task is more straightforward. Experimental results over 35 high dimensional datasets show that the proposed method significantly outperforms the static selection baseline and most evaluated dynamic selection techniques when using a diverse ensemble. Moreover, the proposed technique surpassed the contending state-of-the-art techniques over the problems with the highest excess of incompetent classifiers in overlap regions , further suggesting its suitability to deal with challenging local distributions. Code available at: github.com/marianaasouza/gnn_des .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".