Oversampling Techniques in Machine Learning Detection of Credit Card Fraud
Bibliographic record
Abstract
More than ever before, the trend of doing things online has been explored and successfully implemented in many areas, including online shopping, online learning, working online, to name but a few.However, it has brought with it challenges, including the fraudulent use of credit cards in online purchases, the challenge of academic integrity in online learning, especially in doing exams online, and how to keep people in engaged in meetings, when working and studying online, and still give them adequate privacy.This paper deals with the attempt to detect the fraudulent use of credit cards in a timely manner, to avoid as much negative effects in the world of E-commerce and help maintain consumer confidence.Thus, in the current study, machine learning algorithm LightGBM has been used to detect fraudulent credit card transactions from a real-life dataset containing credit card transactions of the customers.The performance of this classifier is compared with two state-of-the-art classifiers -Decision Tree, and Random Forests, which are extensively used for solving such problems.Since there is data imbalance between fraudulent and nonfraudulent class, the data sampling technique used is the Synthetic Minority Oversampling Technique (SMOTE).SMOTE Oversampling performed best on all classifiers and LightGBM obtained precision value of 1 for both fraudulent and non-fraudulent class.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".