https://oldena.lpnu.ua/handle/ntb/52531| Title: | Business-Oriented Feature Selection for Hybrid Classification Model of Credit Scoring |
| Authors: | Chornous, Galyna Nikolskyi, Ihor |
| Affiliation: | Taras Shevchenko National University of Kyiv |
| Bibliographic description (Ukraine): | Chornous G. Business-Oriented Feature Selection for Hybrid Classification Model of Credit Scoring / Galyna Chornous, Ihor Nikolskyi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 397–401. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Chornous G. Business-Oriented Feature Selection for Hybrid Classification Model of Credit Scoring / Galyna Chornous, Ihor Nikolskyi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 397–401. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | hybrid ensemble feature selection binary classification stacking major voting credit scoring R programming |
| Number of pages: | 5 |
| Page range: | 397-401 |
| Start page: | 397 |
| End page: | 401 |
| Abstract: | Application of predictive models on the basis of data mining confirmed its expediency in solving many economic problems. One of the crucial issues is the assessment of the borrower's creditworthiness on the basis of credit scoring models. This paper proposed an ensemble-based technique combining selected base classification models with business-specific feature selection add-on to increase the classification accuracy of real-life case of credit scoring. As the model limitations have been used easy-understandable algorithms on open-source software (R programming). The statistical results proved that hybrid approach for user-defined variables can be more than useful for ensemble binary classification model. It is shown that a great improvement can be reached by applying hybrid approach to feature selection process on additional variables (more descriptive ones that were built on initial features) for this real-life case with limited computational resources. |
| URI: | https://ena.lpnu.ua/handle/ntb/52531 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | https://datahack.analyticsvidhya.com/contest/practice-problem-loanprediction-iii/ http://www.academia.edu/30496678 https://burakhimmetoglu.com/2016/12/01/stacking-models-forimproved-predictions/ |
| References (Ukraine): | [1] A.Q. Kadhim, G.A. El-Refae, and S.F. El-Itter, “Neural Networks in Bank Insolvency Prediction,” International Journal of Computer Science and Network Security, vol 10, no 5, pp. 240–245, 2010. [2] T. Pavlenko, and O. Chernyak, “Credit risk modeling using bayesian networks,” International Journal of Intelligent Systems, vol. 25, issue 4, pp.326–344, 2010. [3] G.O. Chornous, Proactive Management of Socio-Economic Systems Based on Intellectual Data Analysis: Methodology and Models. Kyiv: Kyiv University, 2014. [4] M. Kim, and D. Kang, “Ensemble with neural networks for bankruptcy prediction,” Expert Systems with Applications, vol. 37, issue 4, pp. 3373–3379, 2010. [5] C.-F. Tsau, and J.-W. Wu, “Using neural network ensembles for bankruptcy prediction and credit scoring,” www.sciencedirect.com [6] L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. New York: Wiley and Sons, 2014. [7] L.R. Medsker, Hybrid Intelligent Systems. Boston: Springer, 2013. [8] L. Zernova, The creditworthiness of bank's clients: Analysis and assessment. LAP LAMBERT Academic Publishing, 2016 [9] N. Siddiqi, Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, 2nd ed.. Wiley, 2017. [10] L. Thomas, J. Crook, and D. Edelman, Credit Scoring and Its Applications, 2nd Revised ed. SIAM-Society for Industrial & Applied Mathematics, 2017. [11] H. Chen, M. Jiang, and X. Wang, “Bayesian Ensemble Assessment for Credit Scoring,” 4th International Conference on Industrial Economics System and Industrial Security Engineering (IEIS), 2017. [12] S. Dahiya, S.S. Handa, and N.P. Singh, “Credit Scoring Using Ensemble of Various Classifiers on Reduced Feature Set,” Industrija, vol.43, no.4, pp. 163-172, 2015. [13] A. G. Armaki, M. F. Fallah, M. Alborzi, and A. Mohammadzadeh, “A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers,” Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2073-2082, 2017. [14] S. H. Van, N. N. Ha, and H. N. Thi Bao, “A hybrid feature selection method for credit scoring,” EAI Endorsed Transactions on Contextaware Systems and Applications, vol. 4, issue 1, 09 2016 - 03 2017. [15] H. Xiao, Z. Xiao, and Y. Wang, “Ensemble classification based on supervised clustering for credit scoring,” Applied Soft Computing, vol. 43, pp. 73-86, June 2016. [16] M. Ala'raj and M. F. Abbod, “A new hybrid ensemble credit scoring model based on classifiers consensus system approach,” Expert Systems with Applications, vol. 64, pp. 36-55, December 2016. [17] Analytics Vidhya / Loan Prediction: Practice Problem // https://datahack.analyticsvidhya.com/contest/practice-problem-loanprediction-iii/ [18] K. S. Cho, “Ensemble learning with feature selection for Alzheimer’s disease prediction,” – http://www.academia.edu/30496678, 2016. [19] B. Himmetoglu, “Stacking models for improved predictions” – https://burakhimmetoglu.com/2016/12/01/stacking-models-forimproved-predictions/, 2017 |
| References (International): | [1] A.Q. Kadhim, G.A. El-Refae, and S.F. El-Itter, "Neural Networks in Bank Insolvency Prediction," International Journal of Computer Science and Network Security, vol 10, no 5, pp. 240–245, 2010. [2] T. Pavlenko, and O. Chernyak, "Credit risk modeling using bayesian networks," International Journal of Intelligent Systems, vol. 25, issue 4, pp.326–344, 2010. [3] G.O. Chornous, Proactive Management of Socio-Economic Systems Based on Intellectual Data Analysis: Methodology and Models. Kyiv: Kyiv University, 2014. [4] M. Kim, and D. Kang, "Ensemble with neural networks for bankruptcy prediction," Expert Systems with Applications, vol. 37, issue 4, pp. 3373–3379, 2010. [5] C.-F. Tsau, and J.-W. Wu, "Using neural network ensembles for bankruptcy prediction and credit scoring," www.sciencedirect.com [6] L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. New York: Wiley and Sons, 2014. [7] L.R. Medsker, Hybrid Intelligent Systems. Boston: Springer, 2013. [8] L. Zernova, The creditworthiness of bank's clients: Analysis and assessment. LAP LAMBERT Academic Publishing, 2016 [9] N. Siddiqi, Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, 2nd ed.. Wiley, 2017. [10] L. Thomas, J. Crook, and D. Edelman, Credit Scoring and Its Applications, 2nd Revised ed. SIAM-Society for Industrial & Applied Mathematics, 2017. [11] H. Chen, M. Jiang, and X. Wang, "Bayesian Ensemble Assessment for Credit Scoring," 4th International Conference on Industrial Economics System and Industrial Security Engineering (IEIS), 2017. [12] S. Dahiya, S.S. Handa, and N.P. Singh, "Credit Scoring Using Ensemble of Various Classifiers on Reduced Feature Set," Industrija, vol.43, no.4, pp. 163-172, 2015. [13] A. G. Armaki, M. F. Fallah, M. Alborzi, and A. Mohammadzadeh, "A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers," Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2073-2082, 2017. [14] S. H. Van, N. N. Ha, and H. N. Thi Bao, "A hybrid feature selection method for credit scoring," EAI Endorsed Transactions on Contextaware Systems and Applications, vol. 4, issue 1, 09 2016 - 03 2017. [15] H. Xiao, Z. Xiao, and Y. Wang, "Ensemble classification based on supervised clustering for credit scoring," Applied Soft Computing, vol. 43, pp. 73-86, June 2016. [16] M. Ala'raj and M. F. Abbod, "A new hybrid ensemble credit scoring model based on classifiers consensus system approach," Expert Systems with Applications, vol. 64, pp. 36-55, December 2016. [17] Analytics Vidhya, Loan Prediction: Practice Problem, https://datahack.analyticsvidhya.com/contest/practice-problem-loanprediction-iii/ [18] K. S. Cho, "Ensemble learning with feature selection for Alzheimer’s disease prediction," – http://www.academia.edu/30496678, 2016. [19] B. Himmetoglu, "Stacking models for improved predictions" – https://burakhimmetoglu.com/2016/12/01/stacking-models-forimproved-predictions/, 2017 |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Chornous_G-Business_Oriented_Feature_397-401.pdf | 159.2 kB | Adobe PDF | View/Open | |
| 2018_Chornous_G-Business_Oriented_Feature_397-401__COVER.png | 601.48 kB | image/png | View/Open |
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