Taxonomy of Modelling Strategies for Handling Imbalanced Datasets
Bibliographic record
Abstract
Class imbalanced datasets which represent real world problems are a challenge for training deep learning neural networks as they are designed to handle balanced class distributions.CNN-based topologies for image classification consider class balanced data or very low level imbalanced one for training and perform poorly when the dataset does not satisfy these conditions.This continuous problem is tackled with different strategies and methods but the ones that generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification algorithm and cost-sensitive approaches.In this paper we propose a taxonomy of modelling strategies for handling imbalanced datasets based on current approaches and algorithms.We also extend our recently proposed topology In-Between Layers Modular (IBLM) Residual Neural Network which widens the convolutional layers by adding feature planes interpreted as increase of filter numbers for each convolution layer of our residual module, and adds some topology changes.We demonstrate the IBLM ResNet classification performance on imbalanced dataset using data level preprocessing algorithms, techniques and new ensembles (Borderline SMOTE and K-Means SMOTE).
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".