Employee turnover prediction based on particle swarm optimization – support vector machine

HIGHLIGHTS

  • What: The work is composed of selecting features efficiently and training the model by F-measure values, which are calculated for each decision tree as weights. In this study, a comparison was made between the proposed model with models trained by genetic algorithm (GA) and backpropagation (BP) neural_networks which proved the effectiveness of the model directly. The insufficiency of applying swarm intelligence algorithms to resolve employee turnover prediction problem is alleviated by employing the model proposed in this study.
  • Who: Employee turnover and colleagues from the School of Computer and Electronic Information, Guangxi University, Nanning . . .

     

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