New Recruitment Approach Based on Logistic Regression Model

Ishraq Hatif Abd Almajed, Ghalia Nassreddine and Joumana Younis ()
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Joumana Younis: LITL - Laboratoire Interdisciplinaire des transitions de Lille - UCL FGES - Université Catholique de Lille - Faculté de gestion, économie et sciences - ICL - Institut Catholique de Lille - UCL - Université catholique de Lille - JUNIA - JUNIA - UCL - Université catholique de Lille, UCL - Université catholique de Lille

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Abstract: Artificial intelligence (AI) is a pivotal technological advancement developed by humans with the aim of enhancing the quality of human existence. It signifies the capacity of a computerized machine resembling a robot to execute tasks typically performed by humans and replicate human behavior. Machine learning (ML), a subfield of AI, involves the construction of systems that acquire the ability to make predictions about new output values by leveraging existing data, without the need for human interaction. Currently, ML has been incorporated into various fields, including but not limited to medical diagnosis, image processing, prediction, classification, learning association, commerce, finance, and natural language processing. This project aims to employ ML techniques within the human resource (HR) department. The implemented model will enable the human resources department to effectively identify the most appropriate candidates for a job opening throughout the recruitment process, utilizing a comprehensive dataset and considering many criteria, all without the need for manual intervention. The construction of the model involves the utilization of an authentic dataset comprising recruitment tasks. Initially, the dataset undergoes a process of selecting the most pertinent elements from both pre-existing and extracted factors. These selected factors include educational level, age, and past experience. Furthermore, taking into consideration these aforementioned factors, a decision system is constructed utilizing the Binary classification approach. The logistic regression classifier is utilized in this investigation. Subsequently, the dataset is partitioned into two distinct subsets, namely the training subset and the testing subset. The effectiveness of the model is demonstrated by the utilization of various evaluation metrics, including the confusion matrix, recall, precision, accuracy, and F-measure values.

Keywords: Machine Learning; Human Resource; Recruitment process; Logistic Regression; Performance (search for similar items in EconPapers)
Date: 2024-02-18
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Published in Mesopotamian Journal of Computer Science, 2024, 2024, pp.15-27. ⟨10.58496/MJCSC/2024/002⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04579327

DOI: 10.58496/MJCSC/2024/002

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