Adaptive Ordinal Sample-Weighted Meta-ResNet for Fault Severity Classification Under Class Imbalance
Bibliographic record
Abstract
In intelligent fault diagnosis, fault severity classification with class imbalance remains a tremendous challenge. To simultaneously consider relative natural order in fault severity and class imbalance, we propose the adaptive ordinal sample-weighted meta residual network (AOSW-MRN). The AOSW-MRN model uses a weighting network and a meta-model cloned from the residual network to create a nonlinear weighted mapping. It adaptively learns sample weights from a balanced and clean-label meta-dataset, training a model robust to imbalance and ordinal relationships. We validate its effectiveness in two real-world case studies with different imbalance rates. Experimental results demonstrate that our model outperforms several existing Start-of-the-Art models regardless of classification and regression performance since it considers the ordinality of samples in the feature space.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".