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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120741

    Title: Developing Effective Fraud Detection Methods for Online Auction
    Authors: 張昭憲;劉祐宏;李青芬
    Keywords: fraud detection;feature selection;classification, online auction;e-commerce
    Date: 2020-10-28
    Issue Date: 2021-05-05 12:13:58 (UTC+8)
    Abstract: The past decade has witnessed the rapid growth of online auctions. However, the low cost and anonymity in joining online auctions provided an easy path for fraudsters. The simple binary reputation system promoted by the auction site is clearly not enough to protect consumers from fraud. In view of this, many fraud detection methods have been proposed. Nevertheless, there are still many weaknesses needed to be improved. To help secure the online trading environment, this study aims at developing more effective methods to identify the fraudsters in online auctions. First, a novel selection method is proposed for deriving a concise attribute set used to build efficient detection models, which allow a reduction in detection costs while improving detection accuracy. In addition, a two-stage detection procedure is proposed wherein multiple mutual-complement models are combined for promoting overall detection accuracy. To evaluate the proposed methods, actual auction transaction histories were collected for testing. The experimental results show that these methods can outperform those in the previous work.
    Relation: TANET 2020 台灣網際網路研討會論文集
    Appears in Collections:[Graduate Institute & Department of Business Administration] Proceeding

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