A Supervised Learning Process to Elicit Fraud Cases in Online Auction Sites

Almendra, Vinicius; Enachescu, Denis

doi:10.1109/synasc.2011.15

Cited by 9 publications

(1 citation statement)

References 16 publications

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“…Kauffman and Wood [8] proposed the use of logistic regression to predict reserve-price shilling in online auction scenarios. Almendra and Enachescu [17] presented an overview of the complete process of fraud elicitation: data extraction, manual labeling of textual comments, automatic labeling of textual comments, and seller classification. Almendra and Enachescu proposed a supervised learning process to prevent fraudulent cases in online auction websites.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Online Shopping Experience using Data Mining and Statistical Techniques

Liu¹,

Yang²

2013

JCIT

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With the advance of internet technologies in recent years, online shopping is becoming a popular trend to make purchases compared to the traditional ways. There are many excellent benefits to both the consumers and business conducting online businesses. However, it could also cause great damage to the business due to the increasing number of fraudulent online transactions. In order to improve the online shopping experience, there are great needs to reduce and prevent the fraudulent activities. This paper aims to discuss how the data mining and statistical algorithms can help to identify the fraudulent transactions' characteristics and prevent the fraudulent transactions in real-time.

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Section: Literature Reviewmentioning

confidence: 99%