Vol: 8, Issue: 3 2018

Page: 214-223

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

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 isn’t 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. Wholesale fake watches UK here are at affordable prices.
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