Volume 19, Issue 4 (3-2023)
JSDP 2023, 19(4): 45-60 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Dehklharghani R, Emami H. Verification of unemployment benefits’ claims using Classifier Combination method. JSDP 2023; 19 (4) : 4
URL: http://jsdp.rcisp.ac.ir/article-1-1011-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1011-en.html
University of Bonab
Abstract: (1092 Views)
Unemployment insurance is one of the most popular insurance types in the modern world. The Social Security Organization is responsible for checking the unemployment benefits of individuals supported by unemployment insurance. Hand-crafted evaluation of unemployment claims requires a big deal of time and money. Data mining and machine learning as two efficient tools for data analysis can assist Social Security Organization in automating this process. In this research work, a hybrid supervised learning method is proposed to verify the eligibility of applicants for unemployment. The proposed method takes as input the information of insured individuals, and assigns a numeric score to each applicant through analyzing the input data. Then, claimants are classified into two groups according to those scores: "Qualified” and "Unqualified". The proposed method includes two hybrid strategies: BSA-SVM and combination of confidence values. In BSA-SVM method, backtracking search algorithm (BSA) is used to estimate the prameters of support vector machines (SVM) and improves the classification performance. In the second approach, confidence values extracted from individual classofiers are combined to better classify the input data. Empirical evaluation shows an accuracy of 87% for BSA-SVM and 86% for the second approach.
Article number: 4
Keywords: Unemployment benefits, data mining, machine learning, supervised learning, BSA-SVM, classifier combination.
Type of Study: Research |
Subject:
Paper
Received: 2019/05/3 | Accepted: 2020/05/13 | Published: 2023/03/20 | ePublished: 2023/03/20
Received: 2019/05/3 | Accepted: 2020/05/13 | Published: 2023/03/20 | ePublished: 2023/03/20
References
1. [1] E.W.T.Ngai, Y. Hu, Y.H.Wong, Y.Chen, and X. Sun, "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature," Decis. Support Syst., vol. 50, no. 3, pp. 559-569, 2011. [DOI:10.1016/j.dss.2010.08.006]
2. [2] A. Hosseini, A. Rezaei, " Fraud detection and solutions to deal with it in insurance organizations using data mining (case study: Social Security Organization) ", Social Security Quarterly, vol. 14, no. 1, pp. 111-136.
3. [3] S. M. Taqwa Fard, z. Jafari, "Detecting Fraud in Car Insurance Using Fuzzy Expert System", Information Technology Management, vol. 7, no. 2, pp. 239-258, 2014.
4. [4] A. Ghorbani and S. Farzai, "Fraud Detection in Automobile Insurance using a Data Mining Based Approach, " Int. J. Mechatronics, Electr. Comput. Technol., vol. 8, no. 27, pp. 3764-3771, 2018, doi: IJMEC/10.225163.
5. [5] M. Firouzi, M. Shakuri, L. Kazemi, S. ascetic; "Identifying fraud in car insurance using data mining methods", Insurance Research Journal, vol. 26, no. 3, p. 103-128, 1390.
6. [6] "Viaene, S. and Dedene, G., 2004. Insurance fraud: issues and challenges. Geneva Papers on Risk and Insurance and Practice, 29, pp.313-33.". [DOI:10.1111/j.1468-0440.2004.00290.x]
7. [7] N. Haji Heydari, S. Khalaha and A. Farahi; "Classification of the risk level of car insurance policyholders using data mining algorithms (case study: an insurance company), " Insurance Research Journal, vol. 26, no. 4, p. 107-129, 1390.
8. [8] L. Hosseinzadeh, "Categories of target customers in the insurance industry using data mining", master's thesis, Tarbiat Modares University, 2016.
9. [9] J. Aghabeigi and S. Rezaei, "Validation of credit customers of Melli Bank based on data mining techniques (logistic regression) ", 2016.
10. [10] R. Tehrani and M. F. Shams, "Designing and explaining the credit risk model in the country's banking system, " Journal of Social Sciences and Humanities of Shiraz University
11. [11] M. Mohammad Khan, M. Ismaili, and M. Yarahamdi, "Designing a credit risk assessment model for bank customers using a logistic regression model," 2017.
12. [12] M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, "An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, " Pattern Recognit., vol. 44, no. 8, pp. 1761-1776, 2011. [DOI:10.1016/j.patcog.2011.01.017]
13. [13] Z.-G. Liu, Q. Pan, J. Dezert, and A. Martin, "Combination of classifiers with optimal weight based on evidential reasoning," IEEE Trans. Fuzzy Syst., vol. 26, no. 3, pp. 1217-1230, 2017. [DOI:10.1109/TFUZZ.2017.2718483]
14. [14] M. A. Duval-Poo, J. Sosa-Garcia, A. Guerra-Gandón, S. Vega-Pons, and J. Ruiz-Shulcloper, "A new classifier combination scheme using clustering ensemble, " in Iberoamerican Congress on Pattern Recognition, 2012, pp. 154-161. [DOI:10.1007/978-3-642-33275-3_19]
15. [15]B. Krawczyk and M. Woźniak, "Untrained weighted classifier combination with embedded ensemble pruning, " Neurocomputing, vol. 196, pp. 14-22, 2016. [DOI:10.1016/j.neucom.2016.02.040]
16. [16]R. Pan, T. Yang, J. Cao, K. Lu, and Z. Zhang, "Missing data imputation by K nearest neighbours based on grey relational structure and mutual information, " Appl. Intell., vol. 43, no. 3, pp. 614-632, 2015, doi: 10.1007/s10489-015-0666-x. [DOI:10.1007/s10489-015-0666-x]
17. [17]D. E. N. Frossard, I. O. Nunes, and R. A. Krohling, "An approach to dealing with missing values in heterogeneous data using k-nearest neighbors, " arXiv Prepr. arXiv1608.04037, 2016, [Online]. Available: http://arxiv.org/abs/1608.04037
18. [18]V. Kumar and S. Minz, "Feature selection: a literature review, " Smart Comput. Rev., vol. 4, no. 3, pp. 211-229, 2014, doi: 10.1504/ijise.2013.052279. [DOI:10.1504/IJISE.2013.052279]
19. [19] M. V. Erp, L. G. Vuurpijl, and L.. Schomaker, "An Overview and Comparison of Voting Methods for Pattern Recognition, " in Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition (IWFHR-8), Niagara-onthe-Lake, Canada, 2002, pp. 195-200.
20. [20] L. A. Alexandre, A. C. Campilho, and M. Kamel, "Combining independent and unbiased classifiers using weighted average, " in Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2000, vol. 2, pp. 495-498.
21. [21]R. Yager and L. Liu, Classic Works of the Dempster-Shafer Theory of Belief Functions, vol. 219. 2008. doi: 10.1007/978-3-540-44792-4. [DOI:10.1007/978-3-540-44792-4]
22. [22]P. Civicioglu, "Backtracking Search Optimization Algorithm for numerical optimization problems, " Appl. Math. Comput., vol. 219, no. 15, pp. 8121-8144, 2013. [DOI:10.1016/j.amc.2013.02.017]
23. [23]D. M. W. Powers, "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation, " J. Mach. Learn. Technol., vol. 2, no. 1, pp. 37-63, 2011.
24. [24] M. T. Fard, F. s. Hosseini, and M. Kh. Babaei, "Hybrid Credit Rating Model Using Genetic Algorithms and Fuzzy Expert Systems (Case Study: Qavamin Financial and Credit Institute), " Information Technology Management, vol. 6, no. 1, pp. 31-46, 2013.
25. [25] M. Salehi and A. Katoli, "Choosing the optimal features in order to determine the credit risk of bank customers, " Smart Business Management Studies Quarterly, vol. 6, no. 2, pp. 129-154, 2016.
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
