Margin-Aware Adaptive-Weighted-Loss for Deep Learning Based Imbalanced Data Classification
Bibliographic record
Abstract
In supervised learning algorithms, the class imbalance problem often leads to generating results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal aspect of separating the overlapping classes. This is the reason why most of these methods are prone to overfit on the training data. To this end, we propose a novel loss function, namely margin-aware adaptive-weighted loss. Here, we first use the large margin softmax to leverage intraclass compactness and interclass separability. Further to learn an unbiased representation of the classes, we put forward a dynamically weighted loss for imbalanced data classification. This weight dynamically adapts on every minibatch based on the inverse class frequencies. In addition, it takes care of the hard-to-train samples by using the confidence scores to learn discriminative hidden representations of the data. The overall framework is found to be effective when evaluated on the following two widely used datasets: 1) Canadian Institute for Advanced Research (CIFAR)-10 and 2) Fashion-MNIST. Additional experiments on human against machine and Asia Pacific tele-ophthalmology society 2019 blindness detection datasets prove the robustness of our methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".