Abstract
Labor potential estimation is a complex analysis task of a large data sets of digital profiles of workers. Digital profile is a structure that contains heterogeneous data and has high complexity. The attribute space of digital profiles datasets has a large dimension. Individual features are distinguished by a large variability of types and include qualitative and quantitative characteristics that can be both continuous or discrete. This fact significantly complicates the use of traditional statistical methods and hinder manual selection of machine learning algorithms for labor potential estimation. Automated machine learning (AutoML) can help to deal with this problem. The results of AutoML can significantly depend on the used AutoML library. AutoML libraries differ in the set of algorithms among which they select the best one, in the constrains that are used to stop the search of the optimal solution, in the techniques used for hyperparameters optimization and in metrics that are calculated for the obtained results. In this work we develop an AutoML framework that unites several commonly used AutoML libraries and enables use of common constrains and metrics. Processing dataset about employees’ performance using the developed framework we show the applicability of the AutoML for the task of labor potential estimation.
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Acknowledgments
This work was supported by a state grant (project No. FFZF-2022-0006) and as part of the agreement on the provision of subsidies from the federal budget for financial support for the implementation of the state task for 2023 (registration number of the EGISU NIOKTR card 1122110800010-7 28/11/2022).
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Kovalevsky, V., Stankova, E., Zhukova, N., Ogiy, O., Tristanov, A. (2023). AutoML Framework for Labor Potential Modeling. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13957. Springer, Cham. https://doi.org/10.1007/978-3-031-36808-0_6
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DOI: https://doi.org/10.1007/978-3-031-36808-0_6
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