Asset Details
Do you wish to reserve the book?
Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions
Do you wish to request the book?
Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Dealing with the Class Imbalance Problem in the Detection of Fake Job Descriptions
2021
Overview
In recent years, the detection of fake job descriptions has become increasingly necessary because social networking has changed the way people access burgeoning information in the internet age. Identifying fraud in job descriptions can help jobseekers to avoid many of the risks of job hunting. However, the problem of detecting fake job descriptions comes up against the problem of class imbalance when the number of genuine jobs exceeds the number of fake jobs. This causes a reduction in the predictability and performance of traditional machine learning models. We therefore present an efficient framework that uses an oversampling technique called FJD-OT (Fake Job Description Detection Using Oversampling Techniques) to improve the predictability of detecting fake job descriptions. In the proposed framework, we apply several techniques including the removal of stop words and the use of a tokenizer to preprocess the text data in the first module. We then use a bag of words in combination with the term frequency-inverse document frequency (TF-IDF) approach to extract the features from the text data to create the feature dataset in the second module. Next, our framework applies k-fold cross-validation, a commonly used technique to test the effectiveness of machine learning models, that splits the experimental dataset [the Employment Scam Aegean (ESA) dataset in our study] into training and test sets for evaluation. The training set is passed through the third module, an oversampling module in which the SVMSMOTE method is used to balance data before training the classifiers in the last module. The experimental results indicate that the proposed approach significantly improves the predictability of fake job description detection on the ESA dataset based on several popular performance metrics.
Publisher
Tech Science Press
Subject
