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.

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.

A categorical approach to the semantics of argumentation

1996 ◽  
Vol 6 (2) ◽  
pp. 167-188 ◽  
Author(s):  
Simon Ambler
Keyword(s):  
Large Scale ◽  
Formal Semantics ◽  
Reasoning Under Uncertainty ◽  
Enriched Category ◽  
Closed Category ◽  
Dempster Shafer Theory ◽  
Medical Expert System ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Cartesian Closed

Argumentation is a proof theoretic paradigm for reasoning under uncertainty. Whereas a ‘proof’ establishes its conclusion outright, an ‘argument’ can only lend a measure of support. Thus, the process of argumentation consists of identifying all the arguments for a particular hypothesis φ, and then calculating the support for φ from the weight attached to these individual arguments. Argumentation has been incorporated as the inference mechanism of a large scale medical expert system, the ‘Oxford System of Medicine’ (OSM), and it is therefore important to demonstrate that the approach is theoretically justified. This paper provides a formal semantics for the notion of argument embodied in the OSM. We present a categorical account in which arguments are the arrows of a semilattice enriched category. The axioms of a cartesian closed category are modified to give the notion of an ‘evidential closed category’, and we show that this provides the correct enriched setting in which to model the connectives of conjunction (&) and implication (⇒).Finally, we develop a theory of ‘confidence measures’ over such categories, and relate this to the Dempster-Shafer theory of evidence.

An extension of case-based decision theory by using Dempster–Shafer theory of evidence

2018 ◽  
Vol 13 (1) ◽  
pp. 179-189 ◽  
Author(s):  
Fei Du ◽  
Feiyan Liu
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Future Research ◽  
Traditional Concept ◽  
Content Type ◽  
Dempster Shafer Theory ◽  
Computational Procedures ◽  
Shafer Theory ◽  
Theory Of Evidence ◽  
Case Based

Purpose This study aims to propose a new decision-making method by integrating case-based decision theory and the Dempster–Shafer theory of evidence. Design/methodology/approach The study developed the entire computational procedures for the proposed method and used a numerical example to illustrate its method. Findings The results show that not only the own experiences of the decision-maker but also the opinions of other persons contribute to the selection. Case-based decision theory provides a fundamental technique for the decision-making procedure, and the Dempster–Shafer theory of evidence offers support to deal with the different sources of decision information. Research limitations/implications In case-based decision theory, the utility is a subjective concept, which cannot be measured easily in numbers. Thus, future research should seek a new method to replace the utility. In addition, how to assess the importance of different persons’ experiences and opinions is an important component of this method. Originality/value The contributions of the paper are mainly reflected in three aspects. The first is to expand the traditional concept of “case” of case-based decision theory to multiple sources of cases, which include not only the decision-maker’s own experiences but also other persons’ opinions. The second is to provide a decision-making framework by integrating case-based decision theory and the Dempster–Shafer theory of evidence. The third is to develop the entire computational procedures for the proposed method.

LINGUISTIC AGGREGATION OPERATORS FOR LINGUISTIC DECISION MAKING BASED ON THE DEMPSTER-SHAFER THEORY OF EVIDENCE

2010 ◽  
Vol 18 (03) ◽  
pp. 287-304 ◽  
Author(s):  
J. M. MERIGÓ ◽  
M. CASANOVAS ◽  
L. MARTÍNEZ
Keyword(s):  
Decision Making ◽  
Aggregation Operators ◽  
Weighted Averaging ◽  
New Approach ◽  
Dempster Shafer Theory ◽  
Wide Range ◽  
Different Types ◽  
Linguistic Decision Making ◽  
Shafer Theory ◽  
Theory Of Evidence

In this paper, we develop a new approach for decision making with Dempster-Shafer theory of evidence by using linguistic information. We suggest the use of different types of linguistic aggregation operators in the model. We then obtain as a result, the belief structure — linguistic ordered weighted averaging (BS-LOWA), the BS — linguistic hybrid averaging (BS-LHA) and a wide range of particular cases. Some of their main properties are studied. Finally, we provide an illustrative example that shows the different results obtained by using different types of linguistic aggregation operators in the new approach.

scholarly journals Plato's Cave in the Dempster-Shafer land--the Link between Pignistic and Plausibility Transformations

2017 ◽  
Author(s):  
Chunlai Zhou ◽  
Biao Qin ◽  
Xiaoyong Du
Keyword(s):  
Statistical Inference ◽  
Belief Function ◽  
Belief Functions ◽  
Reasoning Under Uncertainty ◽  
Function Model ◽  
Probability Functions ◽  
Degree Of Belief ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Theory Of Evidence

In reasoning under uncertainty in AI, there are (at least) two useful and different ways of understanding beliefs: the first is as absolute belief or degree of belief in propositions and the second is as belief update or measure of change in belief. Pignistic and plausibility transformations are two well-known probability transformations that map belief functions to probability functions in the Dempster-Shafer theory of evidence. In this paper, we establish the link between pignistic and plausibility transformations by devising a belief-update framework for belief functions where plausibility transformation works on belief update while pignistic transformation operates on absolute belief. In this framework, we define a new belief-update operator connecting the two transformations, and interpret the framework in a belief-function model of parametric statistical inference. As a metaphor, these two transformations projecting the belief-update framework for belief functions to that for probabilities are likened to the fire projecting reality into shadows on the wall in Plato's cave.

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