A Broad Review on Class Imbalance Learning Techniques
Bibliographic record
Abstract
The imbalanced learning issue is related to the performance of learning algorithms in the presence of asymmetrical class distribution. Due to the complex characteristics of imbalanced datasets, learning from such data need new algorithms and understandings to convert efficient large amounts of initial data into suitable datasets. Although several review papers can be found about imbalanced classification problems, none of them contributed an in-depth review of SVM for imbalanced classification problems. To �ll this gap, we present an exhaustive review of existing methods to deal with issues linked with class imbalance learning. The majority of the existing survey addresses only classification tasks. We also describe methods to deal with similar problems in regression tasks. A new taxonomy for class-imbalanced learning techniques is proposed and classified into three parts: (1) Data pre-processing, (2) Algorithmic structures, and (3) Hybrid techniques. The advantages and disadvantages of each type of imbalanced learning technique are discussed. Moreover, we explain the main difficulties in the distribution of imbalanced datasets and discuss the main approaches that have been proposed to tackle these issues. Finally, to stimulate the next research in this area, we emphasize the main opportunities and challenges, which can be useful in research directions for learning algorithms from imbalanced data.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".