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Machine Learning Framework for Multi-Level Classification of Company Revenue

Authors
최정구고인환YESEULJEON김정재Sanghoon Han
Issue Date
Jun-2021
Publisher
Institute of Electrical and Electronics Engineers
Keywords
machine learning; company revenue; human resource
Citation
IEEE Access, v.9, pp.96,739 - 96,750
Journal Title
IEEE Access
Volume
9
Start Page
96,739
End Page
96,750
URI
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/5279
DOI
10.1109/ACCESS.2021.3088874
ISSN
2169-3536
Abstract
The planning and execution of a business strategy are important aspects of the strategic human resource management of a company. In previous studies, machine learning algorithms were used to determine the main factors correlating employees with company performance. In this study, we introduced a method based on machine-learning algorithms for the classification of company revenue. Both annual and integrated datasets were examined to evaluate the classification performance of the framework under both binary and multiclass conditions. The performance of the proposed method was validated using six evaluation metrics: accuracy, precision, recall, F1-score, receiver operating characteristic curve, and area under the curve. As the experimental results indicate, the XGBoost classifier displayed the best classification performance among the three algorithms (XGBoost classifier, stochastic gradient descent classifier, and logistic regression) used in this study. Moreover, we confirmed the important features of the trained XGBoost model in accordance with variables focusing on human resource management studies. These results demonstrate that the proposed framework has strength in terms of both classification and practical implementation. This study provides novel insights into the relationship between employees and the revenue levels of their employer.
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College of Commerce and Economics > Applied Statistics > 1. Journal Articles
College of Liberal Arts > 문과대학 심리학 > 1. Journal Articles

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