Shill投标数据集数据集

Ahmad Alzahrani and Samira Sadaoui alzah234 '@' uregina.ca and sadaouis '@' uregina.ca Department of Computer Science University of Regina Regina, SK, CANADA, S4S 0A2 Data Set Information: Provide all relevant information about your data set. Attribute Information: Record ID: Unique identifier of a record in the dataset. Auction ID: Unique identifier of an auction. Bidder ID: Unique identifier of a bidder. Bidder Tendency: A shill bidder participates exclusively in auctions of few sellers rather than a diversified lot. This is a collusive act involving the fraudulent seller and an accomplice. Bidding Ratio: A shill bidder participates more frequently to raise the auction price and attract higher bids from legitimate participants. Successive Outbidding: A shill bidder successively outbids himself even though he is the current winner to increase the price gradually with small consecutive increments. Last Bidding: A shill bidder becomes inactive at the last stage of the auction (more than 90\% of the auction duration) to avoid winning the auction. Auction Bids: Auctions with SB activities tend to have a much higher number of bids than the average of bids in concurrent auctions. Auction Starting Price: a shill bidder usually offers a small starting price to attract legitimate bidders into the auction. Early Bidding: A shill bidder tends to bid pretty early in the auction (less than 25\% of the auction duration) to get the attention of auction users. Winning Ratio: A shill bidder competes in many auctions but hardly wins any auctions. Auction Duration: How long an auction lasted. Class: 0 for normal behaviour bidding; 1 for otherwise. Relevant Papers: Paper 1: Scraping and Preprocessing Commercial Auction Data for Fraud Classification Paper 2: Clustering and Labeling Auction Fraud Data Citation Request: Alzahrani A, Sadaoui S. Scraping and preprocessing commercial auction data for fraud classification. arXiv preprint [Web link]. 2018 Jun 2. Alzahrani A, Sadaoui S. Clustering and labeling auction fraud data. InData Management, Analytics and Innovation 2020 (pp. 269-283). Springer, Singapore.