MODELING ECONOMIC EFFICIENCY IN HIGHER EDUCATION INSTITUTIONS USING THE K-NEAREST NEIGHBORS ALGORITHM

Authors

  • Begmatova Ziyoda Zaxidjan kizi TUIT, Independent researcher Author

Keywords:

K-Nearest Neighbors (KNN), efficiency, economic activity, higher education, optimization, financial sustainability, resource management, data classification.

Abstract

This paper explores the use of the K-Nearest Neighbors (KNN) algorithm to enhance the efficiency of the economic activities of institutions serving the higher education system. It examines the main indicators influencing the financial sustainability of educational establishments and proposes a methodology for their analysis using KNN. The study reveals that the application of this algorithm allows for a more accurate classification of institutions based on efficiency levels, facilitating the optimization of resource and cost management. The research findings demonstrate the potential of KNN as a tool for improving decision-making quality in financial management within the higher education system.

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References

Altman, N., & Krzywinski, M. (2015). "K-Nearest Neighbors." Nature Methods, 12(1), 1-2. DOI: 10.1038/nmeth.3318

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.

Liu, H., & Motoda, H. (2007). Computational Methods of Feature Selection. Chapman & Hall/CRC

Cortez, P., & Silva, D. (2008). "Using Data Mining to Predict Secondary School Student Performance." Proceedings of the 5th Annual Conference of Education and Information Systems, Technologies and Applications (EISTA).

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Published

2024-11-01