Abstract
Predicting salaries is crucial in business. While prediction models can be trained on large and real salary datasets, they typically lack information regarding professional experience, an essential factor for salary. We investigate various regression techniques for the estimation of professional experience based on data from the Socio-Economic Panel (SOEP) to augment data sets. We further show how to integrate such models into applications and evaluate the usefulness for salary prediction on a large real payroll dataset.
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Notes
- 1.
[4] builds on random forests, the current application is based on a neural network.
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Eichinger, F., Kiesel, J., Dorner, M., Arnold, S. (2023). Estimations of Professional Experience with Panel Data to Improve Salary Predictions. In: Bramer, M., Stahl, F. (eds) Artificial Intelligence XL. SGAI 2023. Lecture Notes in Computer Science(), vol 14381. Springer, Cham. https://doi.org/10.1007/978-3-031-47994-6_46
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DOI: https://doi.org/10.1007/978-3-031-47994-6_46
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