Artificial Bee Colony for Logic Mining in Credit Scoring
DOI:
https://doi.org/10.11113/mjfas.v18n6.2661Keywords:
Hopfield Neural Network, 2 Satisfiability, Artificial Bee Colony, Logic Mining, Default Credit CardAbstract
During the SARS-CoV-2 (Covid-19) pandemic, credit applications skyrocketed unimaginably. Thus, creditors or financial entities were burdened with information overload to ensure they provided the proper credit to the right person. The existing methods employed by financial entities were prone to overfitting and did not provide any information regarding the behavior of the creditor. However, the outcome did not consider the attribute of the creditor that led to the default outcome. In this paper, a swarm intelligence-based algorithm named Artificial Bee Colony has been implemented to optimize the learning phase of the Hopfield Neural Network with 2 Satisfiability-based Reverse Analysis Methods. The proposed hybrid model will be used to extract logical information in the credit data with more than 80% accuracy compared to the existing method. The effectiveness of the proposed hybrid model was evaluated and showed superior results compared to other models.
References
Zhou, Y., Uddin, M.S., Habib, T., Chi, G. & Yuan, K. (2021). Feature Selection in Credit Risk Modeling: An International Evidence. Economic Research-Ekonomska Istraživanja, 34(1), 3064-3091.
Kozodoi, N., Lessmann, S., Papakonstantinou, K., Gatsoulis, Y. & Baesens, B. (2019). A Multi-Objective Approach for Profit-Driven Feature Selection In Credit Scoring. Decision Support Systems, 120, 106-117.
Lappas, P.Z. & Yannacopoulos, A.N. (2021). A Machine Learning approach Combining Expert Knowledge with Genetic Algorithms in Feature Selection for Credit Risk Assessment. Applied Soft Computing, 107, 107391.
Cichy, R.M. & Kaiser, D. (2019). Deep Neural Networks as Scientific Models. Trends in Cognitive Sciences, 23(4), 305-317.
Rodríguez-Sánchez, A.E. (2020). An Artificial Neural Networks approach to Predict Low-velocity impact Forces in an Elastomer Material. Simulation, 96(6), 551-563.
Davoudi, K. & Thulasiraman, P. (2021). Evolving Convolutional Neural Network Parameters through the Genetic Algorithm for The Breast Cancer Classification Problem. Simulation, 97(8), 511-527.
Xu, L., Gao, Q. & Yousefi, N. (2020). Brain tumor Diagnosis Based On Discrete Wavelet Transform, Gray-Level Co-Occurrence Matrix, and Optimal Deep Belief Network. Simulation, 96(11), 867-879.
Hopfield, J.J. & Tank, D.W. (1985). Neural Computation of Decisions in Optimization Problems. Biological Cybernetics, 52(3), 141-152.
Abdullah, W.A.T.W. (1992). Logic Programming on a Neural Network. International Journal of Intelligent Systems, 7(6), 513-519.
Sathasivam, S. & Abdullah, W.A.T.W. (2011). Logic Mining in Neural Network: Reverse Analysis Method. Computing, 91(6), 119-133.
Kho, L.C., Kasihmuddin, M.S.M., Mansor, M.A. & Sathasivam, S. (2020). Logic Mining in League of Legends. Pertanika Journal of Science & Technology, 28(1), 211-225.
Alway, A., Zamri, N.E., Kasihmuddin, M.S.M., Mansor, M.A. & Sathasivam, S. (2020). Palm Oil Trend Analysis via Logic Mining with Discrete Hopfield Neural Network. Pertanika Journal of Science & Technology, 28(3), 967-981.
Jamaludin, S.Z.M., Kasihmuddin, M.S.M., Ismail, A.I.M., Mansor, M.A. & Basir, M.F.M. (2021). Energy Based Logic Mining Analysis with Hopfield Neural Network for Recruitment Evaluation. Entropy, 23(1), 40.
Kasihmuddin, M.S.M., Mansor, M.A. & Sathasivam, S. (2017). Robust Artificial Bee Colony in The Hopfield Network for 2-Satisfiability Problem. Pertanika Journal of Science & Technology, 25.
Mansor, M. A., Kasihmuddin, M. S. M., Jamaluddin, S. Z. M., & Sathasivam, S. (2020). Pattern 2 Satisfiability Analysis via Hybrid Artificial Bee Colony Algorithm as a Learning Algorithm. Communications in Computational and Applied Mathematics, 2.
Kasihmuddin, M.S.M., Jamaludin, S.Z.M., Mansor, M.A., Wahab, H.A., & Ghadzi, S.M.S. (2022). Supervised Learning Perspective in Logic Mining. Mathematics, 10, 915.
Lidbe, A.D., Hainen, A.M. & Jones, S.L. (2017). Comparative Study of Simulated Annealing, Tabu Search, and the Genetic Algorithm for Calibration of the Microsimulation Model. Simulation, 93(1), 21-33.
Zamri, N.E., Mansor, M.A., Kasihmuddin, M.S.M. Alway, A. Jamaludin, S.Z.M. & Alzaeemi, S.A. 2022. Amazon Employees Resources Access Data Extraction via Clonal Selection Algorithm and Logic Mining Approach. Entropy, 22(6), 596.
Mansor, M.A., Kasihmuddin, M.S.M. & Sathasivam, S. (2017). Artificial Immune System Paradigm in the Hopfield Network for 3-Satisfiability Problem. Pertanika Journal of Science & Technology, 25(4), 1173-1188.
Zamri, N.E., Alway, A., Mansor, M.A., Kasihmuddin, M.S.M. & Sathasivam, S. (2020). Modified Imperialistic Competitive Algorithm in Hopfield Neural Network for Boolean Three Satisfiability Logic Mining. Pertanika Journal of Science & Technology, 28(3), 983-1008.
Karaboga, D. (2005). An Idea Based on Honey Bee Swarm for Numerical Optimization (Vol. 200, pp. 1-10). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department.
Kiran, M.S. & Gündüz, M.E.S.U.T. (2013). XOR-Based Artificial Bee Colony Algorithm for Binary Optimization. Turkish Journal of Electrical Engineering and Computer Sciences, 21(8), 2307-2328.
Jia, D., Duan, X. Khan, M.K. 2014. Binary Artificial Bee Colony Optimization using Bitwise Operation. Computers & Industrial Engineering, 76, 360-365.
Kasihmuddin, M.S.M., Mansor, M.A. & Sathasivam, S. (2017). Robust Artificial Bee Colony in The Hopfield Network for 2-Satisfiability Problem. Pertanika Journal of Science & Technology, 25(2).
Sathasivam, S., Mamat, M., Kasihmuddin, M.S.M. & Mansor, M.A. (2020). Metaheuristics approach for Maximum k Satisfiability in Restricted Neural Symbolic Integration. Pertanika Journal of Science & Technology, 28(2), 545-564.
Wolpert, D.H. & Macready, W.G. (1997). No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82.
Goldberg, D. E., & Deb, K. (1991). A Comparative Analysis of Selection Schemes used in Genetic Algorithms. In Foundations of Genetic Algorithms (Vol. 1, pp. 69-93). Elsevier.
Yeh, I.C. & Lien, C.H. (2009). The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients. Expert Systems with Applications, 36(2), 2473-2480.
Hofman, H. Statlog (German Credit Data) Data Set, 1994, https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data) (Accessed: December 2021).
Willmott, C.J., Ackleson, S.G. Davis, R.E. Feddema, J.J. Klink, K.M., Legates, D.R., ... & Rowe, C.M. (1985). Statistics for the Evaluation and Comparison of Models. Journal of Geophysical Research: Oceans, 90(C5), 8995-9005.
Chai, T. & Draxler, R.R. (2014). Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?–Arguments against avoiding RMSE in the Literature. Geoscientific Model Development, 7(3), 1247-1250.
Tayman, J. & Swanson, D.A. (1999). On the Validity of MAPE as a Measure of Population Forecast Accuracy. Population Research and Policy Review, 18(4), 299-322.
Jamaludin, S.Z.M., Mansor M.A., Baharum, A., Kasihmuddin, M.S.M., Wahab, H.A. & Marsani, M.F. (2023). Modified 2 satisfiability reverse analysis method via logical permutation operator. Computers, Materials & Continua, 74, 2853–2870.
Sathasivam, S. (2010). Upgrading Logic Programming in Hopfield Network. Sains Malaysiana, 39(1), 115-118.
Yang, F., Qiao, Y., Huang, C., Wang, S. & Wang, X. (2021). An Automatic Credit Scoring Strategy (ACSS) using Memetic Evolutionary Algorithm and Neural Architecture Search. Applied Soft Computing, 113, 107871.
Jamaludin, S.Z.M., Romli, N.A., Kasihmuddin, M.S.M., Baharum, A., Mansor, M. A., & Marsani, M. F. (2022). Novel logic mining incorporating log linear approach. Journal of King Saud University-Computer and Information Sciences.
Dowling, W.F. & Gallier, J.H. (1984). Linear-time algorithms for testing the satisfiability of propositional Horn formulae. The Journal of Logic Programming, 1, 267-284.
Zamri, N. E., Azhar, S. A., Mansor, M. A., Alway, A., & Kasihmuddin, M. S. M. (2022). Weighted Random k Satisfiability for k= 1, 2 (r2SAT) in Discrete Hopfield Neural Network. Applied Soft Computing, 126, 109312.
Chen, J., Kasihmuddin, M. S. M., Gao, Y., Guo, Y., Mansor, M. A., Romli, N. A., ... & Zheng, C. (2023). PRO2SAT: Systematic Probabilistic Satisfiability logic in Discrete Hopfield Neural Network. Advances in Engineering Software, 175, 103355.
Guo, Y., Kasihmuddin, M. S. M., Gao, Y., Mansor, M. A., Wahab, H. A., Zamri, N. E., & Chen, J. (2022). YRAN2SAT: A novel flexible random satisfiability logical rule in discrete hopfield neural network. Advances in Engineering Software, 171, 103169.
Alway, A., Zamri, N. E., Karim, S. A., Mansor, M. A., Mohd Kasihmuddin, M. S., & Mohammed Bazuhair, M. (2022). Major 2 satisfiability logic in discrete Hopfield neural network. International Journal of Computer Mathematics, 99(5), 924-948.
Karim, S. A., Zamri, N. E., Alway, A., Kasihmuddin, M. S. M., Ismail, A. I. M., Mansor, M. A., & Hassan, N. F. A. (2021). Random satisfiability: A higher-order logical approach in discrete Hopfield Neural Network. IEEE Access, 9, 50831-50845.
Gao, Y., Guo, Y., Romli, N. A., Kasihmuddin, M. S. M., Chen, W., Mansor, M. A., & Chen, J. (2022). GRAN3SAT: Creating Flexible Higher-Order Logic Satisfiability in the Discrete Hopfield Neural Network. Mathematics, 10(11), 1899.
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Copyright (c) 2022 Siti Zulaikha Mohd Jamaludin, Nur Syazwani Sa’ari, Mohd Shareduwan Mohd Kasihmuddin, Muhammad Fadhil Marsani, Nur Ezlin Zamri, Siti Aishah Azhar, Yueling Guo, Mohd. Asyraf Mansor
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
