A Machine Learning Model for Product Fraud Detection Based On SVM
Why this work is in the frame
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Bibliographic record
Abstract
With the rise of IoT technology, more and more companies use this technology for daily work production. This technology will generate large amounts of data during the application process. If data can be used wisely, it will help companies make better decisions. It is very meaningful to establish a model based on supply chain data to determine whether there is fraud in the product transaction process. It can help merchants in the supply chain avoid fraud, default and credit risk, and improve market order. In this paper, we propose a fraud prediction model based on the SVM classification model. Due to the large amount of data provided by the materials, we first perform feature engineering on the data to obtain processed data that can be used for modeling, and then use the SVM classification model algorithm for data classification and regression. Experiments show that the accuracy of the SVM classification model is 98.61. Compared with logistic regression model and naive Bayes model, it has better data classification and regression capabilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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