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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".