Detecting Fraud Job Recruitment Using Features Reflecting from Real-world Knowledge of Fraud

Chiraratanasopha, Boonthida; Chay-intr, Thodsaporn

doi:10.55003/cast.2022.06.22.008

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…At the same time, values that are too low may result in a model that is too simplistic and unfit. Parameters 'n estimator' with values [50, 100], 'learning rate' with values [0.1, 0.5], and 'max depth' with values [3,5] are also evaluated and compared to find the optimal parameter combination for the Gradient Boosting model's best performance.…”

Section: Parameter Tuningmentioning

confidence: 99%

“…This is because information traffic moves very fast in this digital era. Like online job search, this is one of the most efficient ways that many people worldwide use because of the automatic job recruitment information transfer process [5,6]. Companies do not need to spend too much money to announce job recruitment information, and so do prospective job applicants who do not need to spend more money, time, and effort to obtain this information [7,8].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings

Rofik,

Hakim,

Unjung

et al. 2024

matrik

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Online job searching is one of the most efficient ways to do this, and it is widely used by people worldwide because of the automated process of transferring job recruitment information. The easy and fast process of transferring information in job recruitment has led to the rise of fake job vacancy fraud. Several studies have been conducted to predict fake job vacancies, focusing on improving accuracy. However, the main problem in prediction is choosing the wrong parameters so that the classification algorithm does not work optimally. This research aimed to increase the accuracy of fake job vacancy predictions by tuning parameters using GridSearchCV. The research method used was SVM and Gradient Boosting with parameter adjustments to improve the parameter combination and align it with the predicted model characteristics. The research process was divided into preprocessing, feature extraction, data separation, and modeling stages. The model was tested using the EMSCAD dataset. This research showed that the SVM algorithm can achieve the highest accuracy of 98.88%, while gradient enhancement produces an accuracy of 98.08%. This research showed that optimizing the SVM model with GridSearchCV can increase accuracy in predicting fake job recruitment.

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Section: Parameter Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings

Rofik,

Hakim,

Unjung

et al. 2024

matrik

View full text Add to dashboard Cite

show abstract

“…The research conducted by Chiraratanasopha and Chayintr [35] focused more on proposing a set of features that relays more information on the behavior of fraudsters who commit ORF. Their research utilised the EMSCAD dataset and demonstrated that information on the exaggeration and credibility can improve the performance measures of ORF detection models.…”

Section: B Related Work In the Orf Domainmentioning

confidence: 99%

Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries

2022

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The purpose of this study is to investigate the effects of contextual features on automatic detection accuracy of online recruitment frauds in Australian job market. In addition, the study aims to unearth the significance of localisation of such approaches. The study first generates a dataset based on a local and semi-structured advertising platform in Australia. The labelled dataset is then used to train a learning model on several content-based and contextual features. The existence of advertising body in relevant government and non-government registries in Australia, along with the internet presence of the advertiser, were considered as contextual features. The extraction process of such contextual features was automated as well. The study concludes that the inclusion of contextual features improves the performance measures of the automated online recruitment fraud detection model. The practical implication of the study is two-folds. Firstly, the contextual feature space generation engine can be used with any dataset, with minimal localisation efforts. Secondly, such learning models can be used in the back end of online job recruitment portals to detect and prevent online recruitment frauds. The study not only demonstrates the positive impact of using contextual features in fraud detection using a real-life dataset, but it also demonstrates how these contextual features can be extracted automatically from the web, based on localised company registries. The generated dataset has been made available for further research in the domain.

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Identifying fake job posting using selective features and resampling techniques

Afzal

Rustam

Aljedaani

et al. 2023

Multimed Tools Appl

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Detecting Fraud Job Recruitment Using Features Reflecting from Real-world Knowledge of Fraud

Cited by 5 publications

References 22 publications

Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings

Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings

Online Recruitment Fraud Detection: A Study on Contextual Features in Australian Job Industries

Identifying fake job posting using selective features and resampling techniques

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