Handling the Class Imbalance Problem using an improved Sine Cosine Algorithm for Optimal Instance Selection
Bibliographic record
Abstract
Abstract Class imbalance is a significant problem that is biased, exhibiting excellent performance towards the majority classes in the dataset while inhibiting inferior performance toward minority classes. When dealing with real-world issues, in particular healthcare problems, this kind of biased nature affects classification accuracy. Thus, a class imbalance is a danger that directly affects the effectiveness of any classification algorithm. The Improved Binary Sine Cosine Algorithm (IBSCA) has been used in this work to identify the subset of the majority class in the best possible way. The proposed IBSCA makes some enhancements over the conventional Binary Sine Cosine Algorithm (BSCA) to address the issue of premature convergence with the local optimal solutions. Intending to improve the classification accuracy for unbalanced datasets, the proposed IBSCA seeks to identify the optimal collection of instances from the majority class. The advised IBSCA uses a random agent’s location, which tends to devote considerable time to exploration to find the best possible set of instances. By using the geometric Mean (G-Mean) and F-Score to describe the fitness function, the proposed IBSCA aims to solve the multi-objective optimization issue. The most crucial metrics for assessing how well a classifier performs on skewed datasets are G-Meanand F-Score. Additionally, a V-shaped transfer function is used to handle the discrete nature of the class imbalance issue. On 18 datasets with different imbalance ratios taken from the KEEL repository, experimentation is conducted. Comparisons are made between the suggested IBSCA and the traditional BSCA, Binary Particle Swarm Optimization (BPSO), and Binary GreyWolf Optimization (BGWO). Additionally, the performance of the suggested IBSCA is evaluated against the top outcomes from different research papers. Metrics like Sensitivity, F-Score, G-Mean, and Area under Curve (AUC) show that the suggestedIBSCA outperforms the current algorithms. The statistical findings also demonstrate that the suggested IBSCA is more efficient than the other conventional algorithms.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".