Uncertain Reasoning for Detection of Selling Stolen Goods in Online Auctions Using Contextual Information

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Ladislav Beranek
Keyword(s):  
Contextual Information ◽  
Online Auctions ◽  
Uncertain Reasoning ◽  
Multiple Sources ◽  
Additional Information ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Stolen Goods

This work describes the design of a decision support system for detection of fraudulent behavior of selling stolen goods in online auctions. In this system, each seller is associated with a type of certification, namely “proper seller,” “suspect seller,” and “selling stolen goods.” The certification level is determined on the basis of a seller’s behaviors and especially on the basis of contextual information whose origin is outside online auctions portals. In this paper, we focus on representing knowledge about sellers in online auctions, the influence of additional information available from other Internet source, and reasoning on bidders’ trustworthiness under uncertainties using Dempster-Shafer theory of evidence. To demonstrate the practicability of our approach, we performed a case study using real auction data from Czech auction portal Aukro. The analysis results show that our approach can be used to detect selling stolen goods. By applying Dempster-Shafer theory to combine multiple sources of evidence for the detection of this fraudulent behavior, the proposed approach can reduce the number of false positive results in comparison to approaches using a single source of evidence.

REASONING UNDER UNCERTAINTY FOR SHILL DETECTION IN ONLINE AUCTIONS USING DEMPSTER–SHAFER THEORY

2010 ◽  
Vol 20 (07) ◽  
pp. 943-973 ◽  
Author(s):  
FEI DONG ◽  
SOL M. SHATZ ◽  
HAIPING XU
Keyword(s):  
Decision Making ◽  
Online Auctions ◽  
Single Source ◽  
Reasoning Under Uncertainty ◽  
Multiple Sources ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Positive Results

This paper describes the design of a decision support system for shill detection in online auctions. To assist decision making, each bidder is associated with a type of certification, namely shill, shill suspect, or trusted bidder, at the end of each auction's bidding cycle. The certification level is determined on the basis of a bidder's bidding behaviors including shilling behaviors and normal bidding behaviors, and thus fraudulent bidders can be identified. In this paper, we focus on representing knowledge about bidders from different aspects in online auctions, and reasoning on bidders' trustworthiness under uncertainties using Dempster–Shafer theory of evidence. To demonstrate the feasibility of our approach, we provide a case study using real auction data from eBay. The analysis results show that our approach can be used to detect shills effectively and efficiently. By applying Dempster–Shafer theory to combine multiple sources of evidence for shill detection, the proposed approach can significantly reduce the number of false positive results in comparison to approaches using a single source of evidence.

Object Classification Using CaRBS

2011 ◽  
pp. 2850-2854
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Missing Values ◽  
Mathematical Structure ◽  
Uncertain Reasoning ◽  
Uncertain Information ◽  
Data Set ◽  
Conflicting Information ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Combine Information

The notion of uncertain reasoning has grown relative to the power and intelligence of computers. From sources which are uncertain information and/or imprecise data, it is importantly the ability to represent uncertainty and reason about it (Shafer & Pearl, 1990). A very general problem of uncertain reasoning is how to combine information from independent and partially reliable sources (Haenni & Hartmann,forthcoming). With data mining, understanding the confirming and/or conflicting information from characteristics describing objects classified to given hypotheses is affected by their reliability. Further, the presence of missing values compounds the problem, since the reasons for their presence may be external to the incumbent reliability issues (Olinsky, Chen, & Harlow, 2003; West, 2001). These issues are demonstrated here using the classification technique: Classification and Ranking Belief Simplex (CaRBS), introduced in Beynon and Buchanan (2004) and Beynon (2005). CaRBS operates within the domain of uncertain reasoning, namely in its accommodation of ignorance, due to its mathematical structure based on the Dempster-Shafer theory of evidence (DST) (Srivastava & Mock, 2002). The ignorance here encapsulates incompleteness of the data set (presence of missing values), as well as uncertainty in the evidential support of characteristics to the final classification of the objects. This chapter demonstrates that a technique such as CaRBS, through uncertain reasoning, is able to uniquely manage the presence of missing values by considering them as a manifestation of ignorance, as well as allowing the possible unreliability of characteristics to be inherent. Importantly, the described process removes the need to falsely transform the data set in any way, such as through imputation (Huisman, 2000). The example issue of credit ratings considered here has become increasingly influential since its introduction in around 1900 with the Manual of Industrial and Miscellaneous Securities (Levich, Majnoni, & Reinhart, 2002). The rating agencies shroud their operations in particular secrecy, stating that statistical models cannot be used to replicate their ratings (Singleton & Surkan, 1991), hence advocating the need for alternative analyses, including those utilising uncertain reasoning.

An Exposition of Uncertain Reasoning Based Analysis

2011 ◽  
pp. 183-205
Author(s):  
Malcolm J. Beynon ◽  
Cathy Holt ◽  
Gemma Whatling
Keyword(s):  
Information Content ◽  
Computer Modelling ◽  
Numerical Calculations ◽  
Applied Problem ◽  
Uncertain Reasoning ◽  
Total Hip ◽  
Dempster Shafer Theory ◽  
Hip Replacements ◽  
Shafer Theory ◽  
Theory Of Evidence

Uncertain reasoning is closely associated with the pertinent analysis of data where there may be imprecision, inexactness, and uncertainty in its information content. In computer modelling, this should move any analysis to be inclusive of such potential uncertainty, away from the presumption of perfect data to be worked with. The nascent Classification and Ranking Belief Simplex (CaRBS) technique employed in this chapter enables analysis in the spirit of uncertain reasoning. The operational rudiments of the CaRBS technique are based on the Dempster-Shafer theory of evidence, affording the presence of ignorance in any analysis undertaken. An investigation of Total Hip Arthraplasty (THA), concerned with hip replacements, forms the applied problem around which the uncertain reasoning based analysis using CaRBS is exposited. The presented findings include the levels of fit in constructed models, and the contribution of features within the models. Where appropriate, numerical calculations are shown, to illustrate this novel form of analysis.

scholarly journals A MODIFIED D NUMBERS METHODOLOGY FOR ENVIRONMENTAL IMPACT ASSESSMENT

2017 ◽  
Vol 24 (2) ◽  
pp. 653-669 ◽  
Author(s):  
Ningkui WANG ◽  
Daijun WEI
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Previous Method ◽  
Combination Process ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
D Numbers ◽  
Commutative Property

Environmental impact assessment (EIA) is usually evaluated by many factors influenced by various kinds of uncertainty or fuzziness. As a result, the key issues of EIA problem are to rep­resent and deal with the uncertain or fuzzy information. D numbers theory, as the extension of Dempster-Shafer theory of evidence, is a desirable tool that can express uncertainty and fuzziness, both complete and incomplete, quantitative or qualitative. However, some shortcomings do exist in D numbers combination process, the commutative property is not well considered when multiple D numbers are combined. Though some attempts have made to solve this problem, the previous method is not appropriate and convenience as more information about the given evaluations rep­resented by D numbers are needed. In this paper, a data-driven D numbers combination rule is proposed, commutative property is well considered in the proposed method. In the combination process, there does not require any new information except the original D numbers. An illustrative example is provided to demonstrate the effectiveness of the method.

Knowledge reduction in random information systems via Dempster–Shafer theory of evidence

2005 ◽  
Vol 174 (3-4) ◽  
pp. 143-164 ◽  
Author(s):  
Wei-Zhi Wu ◽  
Mei Zhang ◽  
Huai-Zu Li ◽  
Ju-Sheng Mi
Keyword(s):  
Information Systems ◽  
Knowledge Reduction ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence

A Framework for Decision Fusion in Image Forensics Based on Dempster–Shafer Theory of Evidence

2013 ◽  
Vol 8 (4) ◽  
pp. 593-607 ◽  
Author(s):  
Marco Fontani ◽  
Tiziano Bianchi ◽  
Alessia De Rosa ◽  
Alessandro Piva ◽  
Mauro Barni
Keyword(s):  
Image Forensics ◽  
Decision Fusion ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence
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