IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 9 NO. 4 2023
DOI: 10.56201/ijcsmt.v9.no4.2023.pg1.11

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A Naïve Bayes Classification Model for Financial Services Sector Human Resources Selection Process

Efstratia Stasi, Georgios Rigopoulos

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Abstract

This work focuses on automated decision-making in human resources management. Specifically, on the integration of automated decision-making into the core functions of Human Resources Management. The applicability of Naïve Bayes, a classical classifier, in developing a decision support system for HR professionals in the employee selection process is examined. For this purpose, a decision model is developed and evaluated using actual data collected from a company in the financial sector, concerning internal employee applicants for seven vacant job positions in a two-year period. The collected data was anonymized, transformed into the appropriate format, split into training and test sets. The model was trained with the training dataset and evaluated with the test dataset. The results of this study produce useful information concerning the application of this classifier in the employee selection process. The results are promising and demonstrate that a mix of professional expertise along with algorithmic support may optimize the HMR processes.

keywords:

HRM, Automated decision-making, Algorithmic decision-making, Decision Support Model, Employee Selection, Classification

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References:

Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2021, January). A review of machine learning
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Helberger, N., Araujo, T., & Vreese, C. (2020). Who is the fairest of them all? Public
attitudes and expectations regarding automated decision-making. Elsevier.

Kasper, G. (2019, June 7). The Challenges of Algorithm-Based HR Decision-Making for
Personal Integrity. Journal of Business Ethics, 377-392.

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