Enhanced Cluster Ensemble Approach Using Multiple Attributes in Unreliable Categorical Data

In recent years, due to advancement in modern technology and social communication, advertising new job posts has become very common issue in the present world. So, fake job posting prediction task is going to be a great concern for all. Like many other classification tasks, fake job posing prediction leaves a lot of challenges to face. This paper proposed to use different datamining techniques and classification algorithm like KNN, decision tree, support vector machine, naive bayes classifier, random forest classifier , multi-layer perceptron and deep neural network to predict a job post if it is real or fraudulent. We have experimented on Employment Scam Aegean Dataset (EMSCAD) containing18000 samples. Deep neural network as a classifier, performs great for this classification task. We have used three dense layers for this deep neural network classifier. The trained classifier shows approximately 98% classification accuracy (DNN) to predict a fraudulent job post. Index Terms--false job prediction, deep learning, data mining.

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A Comparative Study on the Prediction of Fake Job Posts using Various Data Mining Techniques

Reddy

Dwarakamai

Rahul

et al. 2022

IJARSCT

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Enhanced Cluster Ensemble Approach Using Multiple Attributes in Unreliable Categorical Data

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A Comparative Study on the Prediction of Fake Job Posts using Various Data Mining Techniques

A Comparative Study on the Prediction of Fake Job Posts using Various Data Mining Techniques

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