Exploratory Methods for Imbalanced Data Classification in Online Recruitment Fraud Detection: A Comparative Analysis

Li, Jiaxu; Li, Yunxuan; Han, Hongjian; Lü, Xin

doi:10.1145/3507524.3507537

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research work by Li, et al [33] proposed an exploratory method to classify recruitment data that are imbalanced. The authors proposed a LightGBM ORF detection model by using data sampling and ensemble learning techniques, on the EMSCAD dataset.…”

Section: B Related Work In the Orf Domainmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

Section: B Related Work In the Orf Domainmentioning

confidence: 99%