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Financial Management and Decision Based on Decision Tree Algorithm

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Abstract

In order to make university financial management more scientific, technologies such as data warehouse, data mining technology and decision support system are applied to college affairs decision-making. Aimed at a large amount of financial data generated by the financial management system in colleges and universities, the financial management and decision-making in colleges and universities are realized based on the decision tree algorithm. It provides effective support for the management of colleges and universities. The advantages and disadvantages of clustering algorithm and classification algorithm are analyzed. An improved C4.5 decision tree algorithm based on metric is proposed. Combined with data warehouse, data mining and analysis technology, data-driven thinking is adopted to establish university financial budget and model. Financial management and decision-making in colleges and universities are realized. It is applied to the financial project budget execution progress early warning, and carries on the experiment analysis and the result research. A complete financial data warehouse is built. Data aggregation, data analysis and data mining can reduce the amount of data information of the system. The result shows that this method makes college financial management and decision-making more convenient and effective.

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References

  1. Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based model for optimizing bank lending decisions. Expert Systems with Applications, 80, 75–82.

    Article  Google Scholar 

  2. Oksel, C., Winkler, D. A., Ma, C. Y., Wilkins, T., & Wang, X. Z. (2016). Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches. Nanotoxicology, 10(7), 1001–1012.

    Article  Google Scholar 

  3. Shukla, S., Mishra, P. K., Jain, R., & Yadav, H. C. (2016). An integrated decision making approach for ERP system selection using SWARA and PROMETHEE method. International Journal of Intelligent Enterprise, 3(2), 120–147.

    Article  Google Scholar 

  4. Al-augby, S., Majewski, S., Nermend, K., & Majewska, A. (2016, May). Proposed investment decision support system for stock exchange using text mining method. In Al-Sadeq international conference on multidisciplinary in IT and communication science and applications (AIC-MITCSA) (pp. 1–6). IEEE.

  5. Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), 39–52.

  6. Srinivasan, S., & Kamalakannan, T. (2017). Multi criteria decision making in financial risk management with a multi-objective genetic algorithm. Computational Economics. https://doi.org/10.1007/s10614-017-9683-7.

  7. Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 95, 27–36.

    Article  Google Scholar 

  8. Rodríguez, A. H., Avilés-Jurado, F. X., Díaz, E., Schuetz, P., Trefler, S. I., Solé-Violán, J., et al. (2016). Procalcitonin (PCT) levels for ruling-out bacterial coinfection in ICU patients with influenza: A CHAID decision-tree analysis. Journal of Infection, 72(2), 143–151.

    Article  Google Scholar 

  9. Mahajan, N., & Kaur, B. P. (2016). Analysis of factors of road traffic accidents using enhanced decision tree algorithm. Analysis, 135(6), 1–3.

  10. Batra, M., & Agrawal, R. (2018). Comparative analysis of decision tree algorithms. In B. Panigrahi, M. Hoda, V. Sharma, & S. Goel (Eds.), Nature inspired computing. Advances in Intelligent Systems and Computing, (vol 652). Springer, Singapore. https://doi.org/10.1007/978-981-10-6747-1_4.

  11. Ahmed, F., & Kim, K. Y. (2017). Data-driven weld nugget width prediction with decision tree algorithm. Procedia Manufacturing, 10, 1009–1019.

    Article  Google Scholar 

  12. Kokina, J., Pachamanova, D., & Corbett, A. (2017). The role of data visualization and analytics in performance management: Guiding entrepreneurial growth decisions. Journal of Accounting Education, 38, 50–62.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 71001075), and the Fundamental Research Funds for the Central Universities (Grant No. skqy201739).

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Correspondence to Cheng Luo.

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Jin, M., Wang, H., Zhang, Q. et al. Financial Management and Decision Based on Decision Tree Algorithm. Wireless Pers Commun 102, 2869–2884 (2018). https://doi.org/10.1007/s11277-018-5312-6

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