9 research outputs found
A Divide-and-Conquer Approach for Solving Fuzzy Max-Archimedean t
A system of fuzzy relational equations with the max-Archimedean t-norm composition was considered. The relevant literature indicated that this problem can be reduced to the problem of finding all the irredundant coverings of a binary matrix. A divide-and-conquer approach is proposed to solve this problem and, subsequently, to solve the original problem. This approach was used to analyze the binary matrix and then decompose the matrix into several submatrices such that the irredundant coverings of the original matrix could be constructed using the irredundant coverings of each of these submatrices. This step was performed recursively for each of these submatrices to obtain the irredundant coverings. Finally, once all the irredundant coverings of the original matrix were found, they were easily converted into the minimal solutions of the fuzzy relational equations. Experiments on binary matrices, with the number of irredundant coverings ranging from 24 to 9680, were also performed. The results indicated that, for test matrices that could initially be partitioned into more than one submatrix, this approach reduced the execution time by more than three orders of magnitude. For the other test matrices, this approach was still useful because certain submatrices could be partitioned into more than one submatrix
Detecting Fraudsters in Online Auction Using Variations of Neighbor Diversity
Inflated reputation fraud is a serious problem in online auction. Recent work suggested that neighbor diversity is an effective feature for discerning fraudsters from normal users. However, there exist many different methods to quantify diversity in the literature. This raises the problem of finding the most suitable method to calculate neighbor diversity for detecting fraudsters. We collect four different methods to quantify diversity, and apply them to calculate neighbor diversity. We then use these various neighbor diversities for fraudster detection. Experimental results on a real-world dataset demonstrate that, although these diversities were calculated differently, their performances on fraudster detection are similar. This finding reflects the robustness of neighbor diversity, regardless of how the diversity is calculated
Detecting Fraudsters in Online Auction Using Variations of Neighbor Diversity
Inflated reputation fraud is a serious problem in online auction. Recent work suggested that neighbor diversity is an effective feature for discerning fraudsters from normal users. However, there exist many different methods to quantify diversity in the literature. This raises the problem of finding the most suitable method to calculate neighbor diversity for detecting fraudsters. We collect four different methods to quantify diversity, and apply them to calculate neighbor diversity. We then use these various neighbor diversities for fraudster detection. Experimental results on a real-world dataset demonstrate that, although these diversities were calculated differently, their performances on fraudster detection are similar. This finding reflects the robustness of neighbor diversity, regardless of how the diversity is calculated
Using Neighbor Diversity to Detect Fraudsters in Online Auctions
Online auctions attract not only legitimate businesses trying to sell their products but also fraudsters wishing to commit fraudulent transactions. Consequently, fraudster detection is crucial to ensure the continued success of online auctions. This paper proposes an approach to detect fraudsters based on the concept of neighbor diversity. The neighbor diversity of an auction account quantifies the diversity of all traders that have transactions with this account. Based on four different features of each trader (i.e., the number of received ratings, the number of cancelled transactions, k-core, and the joined date), four measurements of neighbor diversity are proposed to discern fraudsters from legitimate traders. An experiment is conducted using data gathered from a real world auction website. The results show that, although the use of neighbor diversity on k-core or on the joined date shows little or no improvement in detecting fraudsters, both the neighbor diversity on the number of received ratings and the neighbor diversity on the number of cancelled transactions improve classification accuracy, compared to the state-of-the-art methods that use k-core and center weight
Online Auction Fraud Detection in Privacy-Aware Reputation Systems
With a privacy-aware reputation system, an auction website allows the buyer in a transaction to hide his/her identity from the public for privacy protection. However, fraudsters can also take advantage of this buyer-anonymized function to hide the connections between themselves and their accomplices. Traditional fraudster detection methods become useless for detecting such fraudsters because these methods rely on accessing these connections to work effectively. To resolve this problem, we introduce two attributes to quantify the buyer-anonymized activities associated with each user and use them to reinforce the traditional methods. Experimental results on a dataset crawled from an auction website show that the proposed attributes effectively enhance the prediction accuracy for detecting fraudsters, particularly when the proportion of the buyer-anonymized activities in the dataset is large. Because many auction websites have adopted privacy-aware reputation systems, the two proposed attributes should be incorporated into their fraudster detection schemes to combat these fraudulent activities
Improving Fraudster Detection in Online Auctions by Using Neighbor-Driven Attributes
Online auction websites use a simple reputation system to help their users to evaluate the trustworthiness of sellers and buyers. However, to improve their reputation in the reputation system, fraudulent users can easily deceive the reputation system by creating fake transactions. This inflated-reputation fraud poses a major problem for online auction websites because it can lead legitimate users into scams. Numerous approaches have been proposed in the literature to address this problem, most of which involve using social network analysis (SNA) to derive critical features (e.g., k-core, center weight, and neighbor diversity) for distinguishing fraudsters from legitimate users. This paper discusses the limitations of these SNA features and proposes a class of SNA features referred to as neighbor-driven attributes (NDAs). The NDAs of users are calculated from the features of their neighbors. Because fraudsters require collusive neighbors to provide them with positive ratings in the reputation system, using NDAs can be helpful for detecting fraudsters. Although the idea of NDAs is not entirely new, experimental results on a real-world dataset showed that using NDAs improves classification accuracy compared with state-of-the-art methods that use the k-core, center weight, and neighbor diversity
Oscillation of Repeated Max-Weighted Power Mean Compositions of Fuzzy Matrices
In the literature, the powers of a square fuzzy matrix with respect to the max-weighted power mean composition have been shown to always converge. This study considers the max-weighted power mean composition for a sequence of fuzzy matrices. It reveals that the repeated compositions of a sequence of n fuzzy matrices oscillate among n fuzzy matrices once the number of compositions exceeds a certain threshold. The previous finding can be considered as a special case of this study with n = 1