An Adaptive Feature Dimensionality Reduction Technique Based on Random Forest on Employee Turnover Prediction Model

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 906)


This paper is based on the theme of employee attrition where the reasoning behind employee turnover has predicted with the help of machine learning approach. As employee turnover has become a vital issue these days due to heavy work pressure, less salary, less work satisfaction, poor working environment; it’s high time to uphold a better solution on this term. Therefore, we have come up with a prediction model based on machine learning approach where we have used each feature’s respective Random Forest importance weights while threshold based correlated feature merging into each of the single combined variable. Again, we scale specific features to get the correlated matrix of features matrix by defining threshold. Certainly, this newly developed technique has achieved good result for some algorithms compared to Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for the same dataset.


Random forest PCA LDA Dimensionality reduction Classifier 


  1. 1.
    Sikaroudi, E., Mohammad, A., Ghousi, R., Sikaroudi, A.: A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing). J. Ind. Syst. Eng. 8(4), 106–121 (2015)Google Scholar
  2. 2.
    Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)Google Scholar
  3. 3.
    Gao, Y.: Using decision tree to analyze the turnover of employees (2017)Google Scholar
  4. 4.
    Ajit, P.: Prediction of employee turnover in organizations using machine learning algorithms. Algorithms 4(5), C5 (2016)Google Scholar
  5. 5.
    Howley, T., Madden, M.G., O’Connell, M.L., Ryder, A.G.: The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data. Knowl. Based Syst. 19(5), 363–370 (2006)CrossRefGoogle Scholar
  6. 6.
    Maisuradze, M.: Predictive analysis on the example of employee turnoverGoogle Scholar
  7. 7.
    Alam, M., Mohiuddin, K., Hassan, M.M., Islam, M., Allayear, S.: A machine learning approach to analyze and reduce features to a significant number for employee’s turn over prediction model. In: IEEE Computing Conference 2018, London (2018)Google Scholar
  8. 8.
    Fan, C.Y., Fan, P.S., Chan, T.Y., Chang, S.H.: Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals. Expert Syst. Appl. 39(10), 8844–8851 (2012)CrossRefGoogle Scholar
  9. 9.
    L. (n.d.). HR Analytics. Accessed 09 Dec 2017
  10. 10.
    Sklearn.preprocessing.StandardScaler (n.d.). Accessed 01 Oct 2017
  11. 11.
    Sklearn.preprocessing.RobustScaler (n.d.). Accessed 01 Oct 2017
  12. 12.
  13. 13.
  14. 14.
  15. 15.
    Raschka, S.: Python machine learning. Packt Publishing Ltd., Birmingham (2015)Google Scholar
  16. 16.
    Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)Google Scholar
  17. 17.
    Sklearn.model_selection.train_test_split (n.d.). Accessed 10 Dec 2017
  18. 18.
    Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemometr. Intell. Lab. Syst. 2(1–3), 37–52 (1987)CrossRefGoogle Scholar
  19. 19.
    Izenman, A.J.: Linear discriminant analysis. In: Izenman, A.J. (ed.) Modern Multivariate Statistical Techniques. STS, pp. 237–280. Springer, New York (2013). Scholar
  20. 20.
    Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Multimedia and Creative TechnologyDaffodil International UniversityDhakaBangladesh
  2. 2.School of ComputingAsia Pacific University of Technology and InnovationKuala LumpurMalaysia
  3. 3.Department of Computer Science and EngineeringBRAC UniversityDhakaBangladesh

Personalised recommendations