A Grey Wolf Optimization Improved Deep Belief Network for Employee Attrition Prediction

A Grey Wolf Optimization Improved Deep Belief Network for Employee Attrition Prediction
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© 2021 by IJETT Journal
Volume-69 Issue-10
Year of Publication : 2021
Authors : Usha.P.M, Dr. N.V. Balaji
DOI :  10.14445/22315381/IJETT-V69I10P210

How to Cite?

Usha.P.M, Dr. N.V. Balaji, "A Grey Wolf Optimization Improved Deep Belief Network for Employee Attrition Prediction," International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 72-81, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I10P210

Abstract
The proposed study enables machine learning techniques to predict the probability of employee attrition. The study improves the accuracy of employee attrition prediction by developing an enhanced model using a Deep belief network or DBN. A deep belief network is a form of deep neural network made up of several layers of variables that are latent or hidden. This work uses the Restricted Boltzmann machine, which creates a stack of the network to analyze the pattern of the attrition dataset. The parameters involved in Deep Belief Network are fine-tuned by adapting a novel behavioral inspiration algorithm instead of random assignment of the values. The algorithm used here is the metaheuristic Grey Wolf algorithm which is an optimization algorithm that imitates the hunting behavior of Grey Wolves. Thus, it will increase the performance of the proposed model.

Keywords
Machine learning, Attrition prediction, Restricted Boltzmann machine, Grey Wolf Optimisation

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