2014
DOI: 10.1016/j.eswa.2013.10.033
|View full text |Cite
|
Sign up to set email alerts
|

Detecting online auction shilling frauds using supervised learning

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
21
0

Year Published

2015
2015
2024
2024

Publication Types

Select...
4
4
1

Relationship

0
9

Authors

Journals

citations
Cited by 46 publications
(21 citation statements)
references
References 17 publications
0
21
0
Order By: Relevance
“…The research activity on simultaneous auctions is mainly focused on finding solutions to problems related to: bidding policy (Fornara & Gambardella, 2001;Gerding, Dash, Yuen, & Jennings, 2006;Preist, Bartolini, & Phillips, 2001), which is concerned with issues such as deciding on which auctions to bid in, as well as how much to bid; cross-bidding (Kayhan, McCart, & Bhattacherjee, 2010;McCart, Kayhan, & Bhattacherjee, 2009), which studies the behavior of participating simultaneously in multiple auctions that offer the same good; and collusion (Brusco & Lopomo, 2002;Sherstyuk & Dulatre, 2008;Tsang, Koh, Dobbie, & Alam, 2014), which studies participants' ability to conduct secretive cooperative agreements intended to distort the competition.…”
Section: An Overview Of Auctionsmentioning
confidence: 99%
“…The research activity on simultaneous auctions is mainly focused on finding solutions to problems related to: bidding policy (Fornara & Gambardella, 2001;Gerding, Dash, Yuen, & Jennings, 2006;Preist, Bartolini, & Phillips, 2001), which is concerned with issues such as deciding on which auctions to bid in, as well as how much to bid; cross-bidding (Kayhan, McCart, & Bhattacherjee, 2010;McCart, Kayhan, & Bhattacherjee, 2009), which studies the behavior of participating simultaneously in multiple auctions that offer the same good; and collusion (Brusco & Lopomo, 2002;Sherstyuk & Dulatre, 2008;Tsang, Koh, Dobbie, & Alam, 2014), which studies participants' ability to conduct secretive cooperative agreements intended to distort the competition.…”
Section: An Overview Of Auctionsmentioning
confidence: 99%
“…This is due to the inaccessibility to fraud data as the organizers jam them. Additionally, using artificial data is not recommended because they do not represent the real bidding behavior [13], [24]. Several techniques exist to handle this Imbalanced Learning Problem and those are grouped to two categories: data-level approach (Data Sampling) and algorithm-level approach (Cost Sensitive Learning) [1].…”
Section: Sampling Of Sb Datasetmentioning
confidence: 99%
“…Shill bidding detection and prevention mechanisms have been proposed by researchers [1,2,4,5,7,8,11,[15][16][17][18]. There is now common consensus of the common strategies shill bidders behave in.…”
Section: Introductionmentioning
confidence: 99%