Machine learning and Feature Selection: Applications in Business Management

Authors

  • Siyao Chen University of Connecticut
  • Abhit Daiya University of Connecticut
  • Rubhrat Deb Wildlife Institute of India

DOI:

https://doi.org/10.70393/6a6574626d.323438

ARK:

https://n2t.net/ark:/40704/JETBM.v1n6a05

Disciplines:

Business

Subjects:

Business Strategy

References:

18

Keywords:

Machine Learning, Feature Selection, Business Management

Abstract

In recent years, we have a higher demand for machine learning models in the field of economics and business managment. We have a higher demand for quality of the features used for training. In this process, feature selection plays a key role in identifying the most meaningful features from a dataset while we perfrom various business tasks. Feature selection is not just a technical exercise; it also has profound implications for the transparency and explainability of machine learning models. This study aims to be a valuable resource for both academic and industry experts, offering insights that connect theoretical knowledge with practical implementation. And this paper also highlights the potential applications and significance of feature selection across industries like business, finance, and other real-world scenarios. And it aims to explore deep in the feature selection, showing that its impact on model performance and its role in various domains.

Author Biographies

Siyao Chen, University of Connecticut

University of Connecticut, USA.

Abhit Daiya, University of Connecticut

University of Connecticut, USA.

Rubhrat Deb, Wildlife Institute of India

Wildlife Institute of India, India.

References

Che, C., & Tian, J. (2024). Game Theory: Concepts, Applications, and Insights from Operations Research. Journal of Computer Technology and Applied Mathematics, 1(4), 53-59.

Lee, I., & Shin, Y. J. (2020). Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), 157-170.

Che, C., & Tian, J. (2024). Maximum flow and minimum cost flow theory to solve the evacuation planning. Advances in Engineering Innovation, 12, 60-64.

Cheng, X. (2024). A Comprehensive Study of Feature Selection Techniques in Machine Learning Models.

Taha, A., Cosgrave, B., & Mckeever, S. (2022). Using feature selection with machine learning for generation of insurance insights. Applied Sciences, 12(6), 3209.

Chen, L. H., & Hsiao, H. D. (2008). Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study. Expert systems with applications, 35(3), 1145-1155.

Cheng, X. (2024). Machine Learning-Driven Fraud Detection: Management, Compliance, and Integration. Academic Journal of Sociology and Management, 2(6), 8-13.

Di Mauro, M., Galatro, G., Fortino, G., & Liotta, A. (2021). Supervised feature selection techniques in network intrusion detection: A critical review. Engineering Applications of Artificial Intelligence, 101, 104216

Che, C., & Tian, J. (2024). Methods comparison for neural network-based structural damage recognition and classification. Advances in Operation Research and Production Management, 3, 20-26.

Zhao, Z., Anand, R., & Wang, M. (2019, October). Maximum relevance and minimum redundancy feature selection methods for a marketing machine learning platform. In 2019 IEEE international conference on data science and advanced analytics (DSAA) (pp. 442-452). IEEE.

Che, C., & Tian, J. (2024). Understanding the Interrelation Between Temperature and Meteorological Factors: A Case Study of Szeged Using Machine Learning Techniques. Journal of Computer Technology and Applied Mathematics, 1(4), 47-52.

Tian, J., & Che, C. (2024). Automated Machine Learning: A Survey of Tools and Techniques. Journal of Industrial Engineering and Applied Science, 2(6), 71-76.

Li, Y., Li, T., & Liu, H. (2017). Recent advances in feature selection and its applications. Knowledge and Information Systems, 53, 551-577.

Che, C., & Tian, J. (2024). Leveraging AI in Traffic Engineering to Enhance Bicycle Mobility in Urban Areas. Journal of Industrial Engineering and Applied Science, 2(6), 10-15.

Cheng, X. (2024). Investigations into the Evolution of Generative AI. Journal of Computer Technology and Applied Mathematics, 1(4), 117-122.

Bose, I., & Mahapatra, R. K. (2001). Business data mining—a machine learning perspective. Information & management, 39(3), 211-225.

Cheng, X., & Che, C. (2024). Optimizing Urban Road Networks for Resilience Using Genetic Algorithms. Academic Journal of Sociology and Management, 2(6), 1-7.

Cheng, X., & Che, C. (2024). Interpretable Machine Learning: Explainability in Algorithm Design. Journal of Industrial Engineering and Applied Science, 2(6), 65-70.

Downloads

Published

2024-12-16

How to Cite

Chen, S., Daiya, A., & Deb, R. (2024). Machine learning and Feature Selection: Applications in Business Management. Journal of Economic Theory and Business Management, 1(6), 33–38. https://doi.org/10.70393/6a6574626d.323438

Issue

Section

Articles

ARK