Machine Learning Framework for Multi-Level Classification of Company Revenue

Jung Gu Choi, Inhwan Ko, Jeongjae Kim, Yeseul Jeon, Sanghoon Han

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Article number9453852
Pages (from-to)96739-96750
Number of pages12
JournalIEEE Access
Publication statusPublished - 2021

Bibliographical note

Funding Information:
This work was supported in part by the Ministry of Education of the Republic of Korea, and in part by the National Research Foundation of Korea (NRF) under Grant 2020S1A5A2A03042694.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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