K Nearest Gaussian-A model fusion based framework for imbalanced classification with noisy dataset
Bibliographic record
Abstract
Data quality issues such as data imbalance and data noise have great impact on the performances of many classifiers. Althoughthe co-existence of imbalance and noise appears in many real world datasets, the issue of imbalance and noise have mostlybeen treated separately due to their different causes and problematic consequences. However, doing so may ignore the mutualeffects thus may not achieve optimal classification performance. In this research, we propose a model fusion based framework,termed K Nearest Gaussian (KNG) to tackle the imbalance and noise issues jointly. KNG employs generative modeling method(GMM) to extract the data characteristics from the training data which are less sensitive to data imbalance and noise. The datacharacteristics are then used to establish Gaussian confidence regions which are used to achieve final classification in a K nearestneighbor (KNN) manner. Experiments on seven UCI benchmark datasets and one medical imaging dataset show KNG methodgreatly outperforms traditional classification methods in dealing with imbalanced classification problems with noisy dataset.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".