A Sequential Ensemble Classification (SEC) System for Tackling the Problem of Unbalance Learning: A Case Study
Bibliographic record
Abstract
In this paper we propose a Sequential Ensemble Classification (SEC) technique which is designed to tackle the problem of learning from a data set with an extremely unbalanced distribution of instances among the classes. This system employs a specific decomposition technique that reduces the degree of unbalance in the data by transforming multi-class problem into a sequence of binary class problems. We investigate two different implementations of the proposed method, one based on an ensemble of homogeneous classifiers and a second based on a heterogeneous ensemble of classifiers. A real-world medical data set has been chosen as a case study for the investigation of the proposed method. The data is highly unbalanced, consists of a wide range of class values, some of which contain only a few instances, and which is voluminous. Our experimental results show that both schemes of the SEC system are able to outperform standalone classifiers, with the highest performance being achieved by the homogeneous design of the system.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".