Application of Machine Learning for Online Reputation Systems

Ahmad, Alqwadri,; Azzeh, Mohammad; Almasalha, Fadi

doi:10.1007/s11633-020-1275-7

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…Table 3 confirms that our method is superior to the rest of methods, as we note sensitivity increased by 28.82%, 24.65%, and 14.64% over RUS, and CBUFN, respectively. One of the explanations for KNN superiority is that KNN uses similarity measures to select the nearest instances [ 45 , 46 ] and SBS selects the similar instances in the dataset, which helps the ML algorithms to learn better. Finally, our method clearly outperformed using ANN, where the accuracy reached 0.943.…”

Section: Resultsmentioning

confidence: 99%

Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS)

Ahmad

Kasasbeh

Rawashdeh

2022

Int. j. inf. tecnol.

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Credit card fraud is a growing problem nowadays and it has escalated during COVID-19 due to the authorities in many countries requiring people to use cashless transactions. Every year, billions of Euros are lost due to credit card fraud transactions, therefore, fraud detection systems are essential for financial institutions. As the classes’ distribution is not equally represented in the credit card dataset, the machine learning trains the model according to the majority class which leads to inaccurate fraud predictions. For that, in this research, we mainly focus on processing unbalanced data by using an under-sampling technique to get more accurate and better results with different machine learning algorithms. We propose a framework that is based on clustering the dataset using fuzzy C-means and selecting similar fraud and normal instances that have the same features, which guarantees the integrity between the data features.

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Section: Resultsmentioning

confidence: 99%

Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS)

Ahmad

Kasasbeh

Rawashdeh

2022

Int. j. inf. tecnol.

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“…Scholars have begun to explore the use of machine learning technology for reputation evaluation in recent years. For example, Al Quadri et al [28] used machine learning methods to predict the reliability of consumers from their data. Rantanen et al [29] constructed an index system from the dimensions of quality, reliability, responsibility, success, pleasure, and innovation.…”

Section: Research On Reputation Evaluation Methodmentioning

confidence: 99%

“Dimension Reduction: Feature Subset” Method for Selecting the Best Index Combination in Reputation Evaluation of Crowdsourcing Participants

Huang

Zheng

Wei

2022

Mobile Information Systems

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An effective reputation evaluation mechanism is an essential guarantee for the crowdsourcing mode's healthy, orderly, and rapid development. Aiming at the problems of unsound reputation evaluation mechanism, single reputation evaluation index, and poor discrimination ability of crowdsourcing platforms a “dimension reduction feature subset” method for selecting the best reputation evaluation index combination of crowdsourcing participants is proposed. This method first selects the best dimensionality reduction method by empirical method, then uses the classifier as the evaluation function of feature selection, and uses the sequential backward selection strategy (SBS) to select the feature subset and reputation evaluation algorithm with the best classification performance. The experimental results show that the reputation evaluation method of crowdsourcing participants based on ReliefF-SVM has the best performance in terms of accuracy, F1 measure, and stability and can select a comprehensive, objective, and effective evaluation index combination to distinguish the reputation status of crowdsourcing participants.

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“…Suppose there are two outcomes of interaction i named as α and β then mathematically we can write it as i.α, i.β (6) If the observed number of outcomes are denoted by n then it can be expressed as , in.α, in.β (7)…”

Section: Defense Mechanismmentioning

confidence: 99%

“…An approach towards evaluating content credibility is to evaluate the ranking and rating associated with the contributor of that information. Reputation Systems [6]are widely employed to calculate these ratings or ranks. However, these rating and rankings can be manipulated through reputation attacks [7], that can increase the reputation rank of fake or wrong content.…”

Section: Introductionmentioning

confidence: 99%

Defense Mechanism Against Attacks Promoting Spread Of Wrong Information

Mahmood,

Akif,

Ashraf

et al. 2024

Preprint

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The rise in dependency upon information present on web and social networks has increased the importance of content credibility systems. These sys- tems can help the users to make a right decision related to buying a product, utiliz- ing a service and etc. Application areas including social network blogs of different subjects such as health, food, education, politics and product review/ratings some- times suffer incredible and wrong information flow. The content credibility sys- tems can help users to identify the credible information on these various forums. Recently, researchers have proposed certain mechanisms for these systems, out of which reputation based systems have gained most attention. However, reputa- tion systems are vulnerable to reputation based attacks, like Sybil, Slandering, and Whitewash promoting spread of wrong information. The authors have proposed a defense mechanism against these attacks that is based upon Bayesian Model. The proposed defense mechanism provides protection against these attacks so that fake or wrong information could be prevented. The authors evaluated the proposed mechanism in three different scenarios and presented the results in terms of pre- cision, recall and rate of change in rank. The results reveal almost 88%prevention against these attacks in comparison to the baseline systems.

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Application of Machine Learning for Online Reputation Systems

Cited by 13 publications

References 30 publications

Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS)

Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS)

“Dimension Reduction: Feature Subset” Method for Selecting the Best Index Combination in Reputation Evaluation of Crowdsourcing Participants

Defense Mechanism Against Attacks Promoting Spread Of Wrong Information

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