Semi-supervised Classification of Fraud Data in Commercial Auctions
Bibliographic record
Abstract
Given the magnitude of monetary transactions at auction sites, they are very attractive to fraudsters and scam artists. Shill bidding (SB) is a severe fraud in e-auctions, which occurs during the bidding period and is driven by modern-day technology and clever scammers. SB does not produce any obvious evidence, and it is often unnoticed by the victims. The lack of availability of training datasets for SB and the difficulty in identifying the behavior of sophisticated fraudsters hinder research on SB detection. To safeguard consumers from dishonest bidders, we were incentivized to investigate semi-supervised classification (SSC) for the first time, which is the most suitable approach to solving fraud classification problems. In this study, we first introduce two new SB patterns, and then based on a total of nine SB patterns, we build an SB dataset from commercial auctions and bidder history data. SSC requires the labeling of a few SB data samples, and to this end, we propose an anomaly detection method based on data clustering. We addressed the skewed class distribution with a hybrid data sampling method. Our experiments in training several SSC models show that using primarily unlabeled SB data with a few labeled SB data improves predictive performance when compared to that of supervised models.
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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 | 0.005 | 0.011 |
| 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 | 0.002 | 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".