Abstract
Tax management is a critical aspect of financial governance, and its effective implementation requires accurate and efficient data analysis. Feature selection, as a subfield of machine learning and data analytics, offers promising techniques for identifying the most relevant and informative variables in tax management. This paper explores the concept of feature selection in the context of tax management, highlighting its importance, benefits, and challenges. It discusses various feature selection methods and algorithms, their application to tax data, and their potential impact on improving tax compliance, risk assessment, and fraud detection. The paper concludes with recommendations for implementing feature selection in tax management systems to enhance efficiency and accuracy.
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Ha, VS., Bao, H.N.T. (2024). Feature Selection in Tax Management: Enhancing Efficiency and Accuracy. In: Nguyen, T.H.N., Burrell, D.N., Solanki, V.K., Mai, N.A. (eds) Proceedings of the 4th International Conference on Research in Management and Technovation. ICRMAT 2023. Springer, Singapore. https://doi.org/10.1007/978-981-99-8472-5_23
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DOI: https://doi.org/10.1007/978-981-99-8472-5_23
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