Absenteeism Prediction: A Comparative Study Using Machine Learning Models - Dogruyol, Kagan; Sekeroglu, Boran (Springer International Publishing Ag, 2020)
Solidity of companies or institutions is related to several factors but mostly to absenteeism. Taking annual leave or pre-determined absent days of personnel may be covered by others however, unexpected absenteeism causes irredeemably poor results. Prediction of the correlation between this predetermined and unexpected absenteeism is a challenging task and includes nonlinear relationship. Neural Network based Machine Learning models are built to solve this kind of non-linear problems by using their non-deterministic nature. In this research, three neural network models; Backpropagation, Radial Basis Function and Long-Short Term Memory neural networks, are implemented to solve prediction problem of absenteeism. In addition, a comparative study is conducted between these models. Two experiments with different training ratios and three evaluation criteria are considered and implemented. The experimental results suggested that Long-Short Term Memory neural network has very high prediction rates as 99.9% in prediction problems that consists complex data and it produced superior results than other two neural network models.
10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions (ICSCCW) -- AUG 27-28, 2019 -- Prague, CZECH REPUBLIC
Keywords: Long-Short Term Memory Network; Backpropagation; Radial basis function neural network