The Performance Comparison of Models for Predicting the Risk of Losing Student Loan by Fuzzy Neural Network Method and Multiple Linear  Regression Analysis Method

Tawin Tanawong; Sanya Khruahong; Adirek Roongrungsi

doi:10.14456/nujst.2020.18

Authors

Tawin Tanawong Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand
Sanya Khruahong Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand
Adirek Roongrungsi Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Phitsanulok, 65000, Thailand

DOI:

https://doi.org/10.14456/nujst.2020.18

Keywords:

Fuzzy Neural Network, Multiple Linear Regression Analysis, Student Loan, Fuzzy logic

Abstract

This research presents the comparing the performance of the prediction model of the risk of losing student loan by Fuzzy Neural Network (FNN) and Multiple Linear Regression Analysis (MLR) . We used the preparation of 296 samples, assigned the income variables, which are characterised by Fuzzy attribute and had problem-solving with Dummy variables. Also, we proposed the method of converting fuzzy data by making a fuzzy attribute matching of Neural Network (NN) and Multiple Linear Regression. It calculates the fuzzy linguistic term, the fuzzy language and the Crisp value, and after that have filtered variables with Pearson correlation technique and multiple linear regression which get eight independent variables and RiskForPay is a dependent variable. Results from this research, the appropriate model of Fuzzy Neural Network is a division of learning data, and cross-validation fold is 5 with an accuracy of 83.33% +/- 6.02% which models have 8-5-6 structure, momentum 0.2 and learning rate 0.3. The predictive model with multiple linear regression equations has Root Mean Squared Error: 1.513 +/- 0.000 and Squared Correlation: 0.081, respectively.

References

Astamal, M. A., & Rahimi, R. (2019). Designing an Expert System for Credit Rating of Real Customers of Banks Using Fuzzy Neural Networks. Advances in Mathematical Finance and Applications, 4(1), 89-102. https://doi.org/10.22034/amfa.2019.577561.1128

Haykin, S., (2009). Neural networks and learning machines (3rd ed.). Retrieved from https://cours.etsmtl.ca/sys843/REFS/Books/ebook_Haykin09.pdf

Hoffmann, J. P., & Shafer, K. (2015). Linear regression analysis. Washington, DC: NASW Press.

Lopes-Silva, J. P., Panissa, V., Julio, U. F., & Franchini, E. (2019). Influence of Physical Fitness on Special Judo Fitness Test Performance. Retrieved from https://www.researchgate.net/publication/328876361_Influence_of_Physical_Fitness_on_Special_Judo_Fitness_Test_Performance_-_A_Multiple_Linear_Regression_Analysis

Mendel, J. M. (1995). Fuzzy logic systems for engineering: a tutorial. Institute of Electrical and Electronics Engineers, 83(3), 345-377. https://doi.org/10.1519/JSC.0000000000002948

Osowski, S., & Linh, T. H. (2001). ECG beat recognition using fuzzy hybrid neural network. Institute of Electrical and Electronics Engineers, 48(11), 1265-1271. Retrieved from https://ieeexplore.ieee.org/document/959322

Souza, P. V. C. (2018). Regularized fuzzy neural networks for pattern classification problems. International Journal of Applied Engineering Research, 13(5), 2985-2991. Retrieved from https://www.ripublication.com/ijaer18/ ijaerv13n5_122.pdf

Suzuki, T., Shimoda, T., Takahashi, N., Tsutsumi, K., Samukawa, M., Yoshimura, S., & Ogasawara, K. (2018). Factors Affecting Bone Mineral Density Among Snowy Region Residents in Japan: Analysis Using Multiple Linear Regression and Bayesian Network Model. Interactive Journal of Medical Research, 7(1), 10. https://doi.org/10.2196/preprints.8555

Tang, J., Liu, F., Zhang, W., Ke, R., & Zou, Y. (2018). Lane-changes prediction based on adaptive fuzzy neural network. Expert Systems with Applications, 91, 452-463. https://doi.org/10.1016/j.eswa.2017.09.025

Wai, R.-J., Chen, M.-W., & Liu, Y.-K. (2015). Design of adaptive control and fuzzy neural network control for single-stage boost inverter. Institute of Electrical and Electronics Engineers, 62(9), 5434-5445. Retrieved from https://ieeexplore.ieee.org/document/7054543

Wai, R.-J., Chen, M.-W., & Yao, J.-X. (2016). Observer-based adaptive fuzzy-neural-network control for hybrid maglev transportation system. Neurocomputing, 175, 10-24. https://doi.org/10.1016/j.neucom.2015.10.006

Wang, G., Hao, J., Ma, J., & Huang, L. (2010). A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Systems with Applications, 37(9), 6225-6232. https://doi.org/10.1016/j.eswa.2010.02.102