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Singh, M., Varshney, K.R., Wang, J., Mojsilovic, A., Gill, A.R., Faur, P.I. and Ezry, R. (2012) An Analytics Approach for Proactively Combating Voluntary Attrition of Employees. 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), Brussels, 10-13 December 2012, 317-323. https://doi.org/10.1109/ICDMW.2012.136

has been cited by the following article:

• TITLE: Predicting the Perceived Employee Tendency of Leaving an Organization Using SVM and Naive Bayes Techniques

  AUTHORS: Ijeoma Lilian Emmanuel-Okereke, Sylvanus Okwudili Anigbogu

  KEYWORDS: Classification, Job Exit, Machine Learning, Naive Bayes, Support Vector Machine

  JOURNAL NAME: Open Access Library Journal, Vol.9 No.3, March 28, 2022

  ABSTRACT: There are several experienced and highly skilled employees considered to be assets in every organization; a good and flexible working environment is required to retain them. The perceived exit of well-skilled and highly experienced employees may result in financial losses, poor sales with customers’ dissatisfaction and produced a low turnover. It also led to low production and output. The existing methods of operation lack the merit in producing accurate and reliable results. It could not generalize well with testing datasets and results in the problem of model over-fitting. Little or no work has been done in the area of predicting the perceived employee tendency of leaving an organization using Support Vector Machine (SVM) and Naive Bayes (NB) algorithm. The implemention was done using Python (Spyder IDE) in ANACONDA. In this paper, a model which is capable of predicting the perceived employee tendency of exiting an organization was developed using the support vector machine and the Naive Bayesian machine learning algorithm. The adopted techniques improved the prediction accuracy and generalized well with testing datasets in overcoming the problem of over-fitting. It also reduced the sudden occurrence of experienced and skilled employees leaving an organization. We adopted the SVM and NB to effectively handle overlapping and reduce data misclassification errors that can work well with a limited number of the dataset. The proposed NB model was trained, successfully tested and evaluated using the same dataset in comparison with the SVM technique. The experimental results of NB model produced 100% prediction accuracy with a 0.0000 RMSE error value in comparison with the SVM which gave a 97.00% success rate and 0.0258 RMSE value.