EFN-SMOTE: An effective oversampling technique for credit card fraud detection by utilizing noise filtering and fuzzy c-means clustering
Bibliographic record
Abstract
Credit card fraud poses a significant challenge for both consumers and organizations worldwide, particularly with the increasing reliance on credit cards for financial transactions. Therefore, it is crucial to establish effective mechanisms to detect credit card fraud. However, the uneven distribution of instances between the two classes in the credit card dataset hinders traditional machine learning techniques, as they tend to prioritize the majority class, leading to inaccurate fraud pre- dictions. To address this issue, this paper focuses on the use of the Elbow Fuzzy Noise Filtering SMOTE (EFN-SMOTE) technique, an oversampling approach, to handle unbalanced data. EFN-SMOTE partitions the dataset into multiple clusters using the Elbow method, applies noise filtering to each cluster, and then employs SMOTE to synthesize new minority instances based on the nearest majority instance to each minority instance, thereby improving the model’s ability to perceive the decision boundary. EFN-SMOTE’s performance was evaluated using an Artificial Neural Network model with four hidden layers, resulting in significant improvements in classification performance, achieving an accuracy of 0.999, precision of 0.998, sensitivity of 0.999, specificity of 0.998, F-measure of 0.999, and G-Mean of 0.999.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".