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Cost-based feature selection for Support Vector Machines: An application in credit scoring

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Cited by:

  1. Blanquero, Rafael & Carrizosa, Emilio & Molero-Río, Cristina & Romero Morales, Dolores, 2020. "Sparsity in optimal randomized classification trees," European Journal of Operational Research, Elsevier, vol. 284(1), pages 255-272.
  2. Chi Ming Chen & Geoffrey Kwok Fai Tso & Kaijian He, 2024. "Quantum Optimized Cost Based Feature Selection and Credit Scoring for Mobile Micro-financing," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 919-950, February.
  3. Blanquero, R. & Carrizosa, E. & Jiménez-Cordero, A. & Martín-Barragán, B., 2019. "Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm," European Journal of Operational Research, Elsevier, vol. 275(1), pages 195-207.
  4. Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
  5. Zhang, Yishi & Zhu, Ruilin & Chen, Zhijun & Gao, Jie & Xia, De, 2021. "Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data," European Journal of Operational Research, Elsevier, vol. 290(1), pages 235-247.
  6. Revathi Bhuvaneswari & Antonio Segalini, 2020. "Determining Secondary Attributes for Credit Evaluation in P2P Lending," Papers 2006.13921, arXiv.org.
  7. Dawen Yan & Guotai Chi & Kin Keung Lai, 2020. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models," Mathematics, MDPI, vol. 8(8), pages 1-27, August.
  8. Huei-Wen Teng & Michael Lee, 2019. "Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-27, September.
  9. Liu, Wanan & Fan, Hong & Xia, Meng, 2023. "Tree-based heterogeneous cascade ensemble model for credit scoring," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1593-1614.
  10. Jingjing Long & Cuiqing Jiang & Stanko Dimitrov & Zhao Wang, 2022. "Clues from networks: quantifying relational risk for credit risk evaluation of SMEs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-41, December.
  11. Chengbin Wang & Kuangnan Fang & Chenlu Zheng & Hechao Xu & Zewei Li, 2021. "Credit scoring of micro and small entrepreneurial firms in China," International Entrepreneurship and Management Journal, Springer, vol. 17(1), pages 29-43, March.
  12. Zhang, Hao & Shi, Yuxin & Yang, Xueran & Zhou, Ruiling, 2021. "A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance," Research in International Business and Finance, Elsevier, vol. 58(C).
  13. Jiaming Liu & Jiajia Liu & Chong Wu & Shouyang Wang, 2024. "Enhancing credit risk prediction based on ensemble tree‐based feature transformation and logistic regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 429-455, March.
  14. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
  15. Astorino, Annabella & Avolio, Matteo & Fuduli, Antonio, 2022. "A maximum-margin multisphere approach for binary Multiple Instance Learning," European Journal of Operational Research, Elsevier, vol. 299(2), pages 642-652.
  16. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
  17. Baldomero-Naranjo, Marta & Martínez-Merino, Luisa I. & Rodríguez-Chía, Antonio M., 2020. "Tightening big Ms in integer programming formulations for support vector machines with ramp loss," European Journal of Operational Research, Elsevier, vol. 286(1), pages 84-100.
  18. Li, An-Da & He, Zhen & Wang, Qing & Zhang, Yang, 2019. "Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method," European Journal of Operational Research, Elsevier, vol. 274(3), pages 978-989.
  19. Tsai, Chih-Fong & Sue, Kuen-Liang & Hu, Ya-Han & Chiu, Andy, 2021. "Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction," Journal of Business Research, Elsevier, vol. 130(C), pages 200-209.
  20. Sandra Benítez-Peña & Rafael Blanquero & Emilio Carrizosa & Pepa Ramírez-Cobo, 2019. "On support vector machines under a multiple-cost scenario," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 663-682, September.
  21. Lee, In Gyu & Yoon, Sang Won & Won, Daehan, 2022. "A Mixed Integer Linear Programming Support Vector Machine for Cost-Effective Group Feature Selection: Branch-Cut-and-Price Approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1055-1068.
  22. Gao, Zheming & Fang, Shu-Cherng & Luo, Jian & Medhin, Negash, 2021. "A kernel-free double well potential support vector machine with applications," European Journal of Operational Research, Elsevier, vol. 290(1), pages 248-262.
  23. Chengbin Wang & Kuangnan Fang & Chenlu Zheng & Hechao Xu & Zewei Li, 0. "Credit scoring of micro and small entrepreneurial firms in China," International Entrepreneurship and Management Journal, Springer, vol. 0, pages 1-15.
  24. Victor Chang & Sharuga Sivakulasingam & Hai Wang & Siu Tung Wong & Meghana Ashok Ganatra & Jiabin Luo, 2024. "Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers," Risks, MDPI, vol. 12(11), pages 1-33, November.
  25. Kusunoki, Yoshifumi & Tatsumi, Keiji, 2025. "Multi-class support vector machine based on minimization of reciprocal-geometric-margin norms," European Journal of Operational Research, Elsevier, vol. 324(2), pages 580-589.
  26. Maarouf, Abdurahman & Feuerriegel, Stefan & Pröllochs, Nicolas, 2025. "A fused large language model for predicting startup success," European Journal of Operational Research, Elsevier, vol. 322(1), pages 198-214.
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