Efficient sampling techniques for ensemble learning and diagnosing bearing defects under class imbalanced condition
Why this work is in the frame
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Bibliographic record
Abstract
This paper focuses on sampling techniques to rebalance class distribution in a way that major and minor classes reach to almost equal number of the samples. A novel iterative over-sampling technique has been proposed which initially induces the missing values on the set of samples of the minor class and, then, imputes the missing scores to generate new synthetic samples of the minor class, in order to re-balance the class distribution. Two variations of the proposed oversampling framework have been developed which make use of the Expectation Maximization and k-Nearest Neighbors imputation strategies. Moreover, the proposed over-sampling technique, which generates new samples for the minor class, has been integrated with a random under-sampling technique, which aims to simultaneously reduce the number of samples for the major class to speed up the process. The proposed sampling procedures have been used along with the ensemble of classifiers forming a diagnostic system. The constructed diagnostic scheme can efficiently diagnose multiple bearing defects in induction motors under class imbalanced condition.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it