Analysis of Resampling Techniques on Predictive Performance of Credit Card Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Credit card fraud detection has been a very demanding research area due to its huge financial implications and rampant applications in almost every area of life. Credit card fraud datasets are naturally imbalanced by having more legitimate transaction in comparison to the fraudulent transactions.  Literature represents numerous studies that are aimed to balance the skewed datasets. There are two major techniques of resampling in balancing these sets i.e. under-sampling and oversampling. However both under-sampling and oversampling techniques suffer from their own set of problems that can seriously affect the performance of classifiers that have been inducted for credit card studies in the past. Thus to accelerate detection of credit card fraud, it is very important to implement the strategy that could possibly provide better predictive performance. This paper attempts to find out what resampling technique can work best under different skewed distributions for the domain of credit card fraud detection.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it