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

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

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  • DOI: 10.1007/978-981-13-1813-9_27
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Correspondence to Md. Kabirul Islam .

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Islam, M.K., Alam, M.M., Islam, M.B., Mohiuddin, K., Das, A.K., Kaonain, M.S. (2018). An Adaptive Feature Dimensionality Reduction Technique Based on Random Forest on Employee Turnover Prediction Model. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore.

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