Analysing the Efficacy of GWO and PSO in Developing Optimised Model of C5.0 Linked to Association Rules in Determining Optimisers to Predict Employ Attrition
Mehul Shorewala
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Abstract
Predicting the attrition of employee based on 5 selected attributes which are Gender, Distance from
Home, Environment Satisfaction, Work Life Balance and Education Field out of 36 variables present
in the dataset. Application of Grey Wolf Optimisation (GWO) Algorithm and Particle Swarm
Optimisation (PSO) on the model of Decision Tree Algorithm C5.0 which is fed in the inputs of
Associated Rules, using this optimized algorithm for the prediction of employee attrition using IBM
Watson Human Resource Employee Attrition Data. After comparing the efficiency of GWO and PSO,
we have come to a conclusion that time to predict an employee attrition and consumption of RAM
have been optimized with GWO. Employee Attrition is one of the major problems faced by companies
now-a-days. Sometimes, when the long term working employees leave the company, it affects the
relationship of the company with the client and in turn affects the revenue of the company if the
person replacing the old employee isnt able manage a good rapport with the client. The paper can
be used to frame better work policies which will help both the employer and employee. It can be seen
as a mirror to the working conditions of the employees.relojes replicas
Keywords: Apriori Algorithm; Association Technique; C5.0; Data Mining; Decision Tree; Employee Attrition; Entropy; IBM Watson HR; Information gain; Grey Wolf Optimization; Particle Swarm Optimization