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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)

Abstract

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.

Keywords

Random forest PCA LDA Dimensionality reduction Classifier 

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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

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