| DC Field | Value | Language |
|---|
| dc.contributor.author | Chornous, Galyna | |
| dc.contributor.author | Nikolskyi, Ihor | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:05:50Z | - |
| dc.date.available | 2020-06-19T12:05:50Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | 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). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52531 | - |
| dc.description.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. | |
| dc.format.extent | 397-401 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.relation.uri | https://datahack.analyticsvidhya.com/contest/practice-problem-loanprediction-iii/ | |
| dc.relation.uri | http://www.academia.edu/30496678 | |
| dc.relation.uri | https://burakhimmetoglu.com/2016/12/01/stacking-models-forimproved-predictions/ | |
| dc.subject | hybrid | |
| dc.subject | ensemble | |
| dc.subject | feature selection | |
| dc.subject | binary classification | |
| dc.subject | stacking | |
| dc.subject | major voting | |
| dc.subject | credit scoring | |
| dc.subject | R programming | |
| dc.title | Business-Oriented Feature Selection for Hybrid Classification Model of Credit Scoring | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | Taras Shevchenko National University of Kyiv | |
| dc.format.pages | 5 | |
| dc.identifier.citationen | 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). | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [3] G.O. Chornous, Proactive Management of Socio-Economic Systems Based on Intellectual Data Analysis: Methodology and Models. Kyiv: Kyiv University, 2014. | |
| dc.relation.references | [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. | |
| dc.relation.references | [5] C.-F. Tsau, and J.-W. Wu, “Using neural network ensembles for bankruptcy prediction and credit scoring,” www.sciencedirect.com | |
| dc.relation.references | [6] L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. New York: Wiley and Sons, 2014. | |
| dc.relation.references | [7] L.R. Medsker, Hybrid Intelligent Systems. Boston: Springer, 2013. | |
| dc.relation.references | [8] L. Zernova, The creditworthiness of bank's clients: Analysis and assessment. LAP LAMBERT Academic Publishing, 2016 | |
| dc.relation.references | [9] N. Siddiqi, Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, 2nd ed.. Wiley, 2017. | |
| dc.relation.references | [10] L. Thomas, J. Crook, and D. Edelman, Credit Scoring and Its Applications, 2nd Revised ed. SIAM-Society for Industrial & Applied Mathematics, 2017. | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [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. | |
| dc.relation.references | [17] Analytics Vidhya / Loan Prediction: Practice Problem // https://datahack.analyticsvidhya.com/contest/practice-problem-loanprediction-iii/ | |
| dc.relation.references | [18] K. S. Cho, “Ensemble learning with feature selection for Alzheimer’s disease prediction,” – http://www.academia.edu/30496678, 2016. | |
| dc.relation.references | [19] B. Himmetoglu, “Stacking models for improved predictions” – https://burakhimmetoglu.com/2016/12/01/stacking-models-forimproved-predictions/, 2017 | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [3] G.O. Chornous, Proactive Management of Socio-Economic Systems Based on Intellectual Data Analysis: Methodology and Models. Kyiv: Kyiv University, 2014. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [5] C.-F. Tsau, and J.-W. Wu, "Using neural network ensembles for bankruptcy prediction and credit scoring," www.sciencedirect.com | |
| dc.relation.referencesen | [6] L.I. Kuncheva, Combining Pattern Classifiers: Methods and Algorithms. New York: Wiley and Sons, 2014. | |
| dc.relation.referencesen | [7] L.R. Medsker, Hybrid Intelligent Systems. Boston: Springer, 2013. | |
| dc.relation.referencesen | [8] L. Zernova, The creditworthiness of bank's clients: Analysis and assessment. LAP LAMBERT Academic Publishing, 2016 | |
| dc.relation.referencesen | [9] N. Siddiqi, Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, 2nd ed.. Wiley, 2017. | |
| dc.relation.referencesen | [10] L. Thomas, J. Crook, and D. Edelman, Credit Scoring and Its Applications, 2nd Revised ed. SIAM-Society for Industrial & Applied Mathematics, 2017. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [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. | |
| dc.relation.referencesen | [17] Analytics Vidhya, Loan Prediction: Practice Problem, https://datahack.analyticsvidhya.com/contest/practice-problem-loanprediction-iii/ | |
| dc.relation.referencesen | [18] K. S. Cho, "Ensemble learning with feature selection for Alzheimer’s disease prediction," – http://www.academia.edu/30496678, 2016. | |
| dc.relation.referencesen | [19] B. Himmetoglu, "Stacking models for improved predictions" – https://burakhimmetoglu.com/2016/12/01/stacking-models-forimproved-predictions/, 2017 | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 397 | |
| dc.citation.epage | 401 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
