Detection of Online Employment Scam Through Fake Jobs Using Random Forest Classifier

EasyChair Preprint 8717

Abstract

To avoid fraudulent posts for jobs on the internet, an automated tool using machine learning-based classification techniques. Different classifiers are used for checking fraudulent posts on the web and the results of those classifiers are compared for identifying the best employment scam detection model. It helps in detecting fake job posts from an enormous number of posts. Two major types of classifiers, such as single classifiers and ensemble classifiers are considered for fraudulent job post detection. However, experimental results indicate that ensemble classifiers are the best classification to detect scams over single classifiers.

Keyphrases: Ensemble approach, Fake Job, machine learning, online recruitment

@booklet{EasyChair:8717,
  author    = {D. Madhavi and M.Sri Manisha Reddy and M. Ramya},
  title     = {Detection of Online Employment Scam Through Fake Jobs Using Random Forest Classifier},
  howpublished = {EasyChair Preprint 8717},
  year      = {EasyChair, 2022}}