Subhradeep Ray
Research Subjects
Subhradeep Ray has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Akhil Kumar Das
Gour Mahavidyalaya
1 shared publication - Ardhendu Mandal
University of North Bengal
1 shared publication - Arijit Bhattacharya
Gour Mahavidyalaya
1 shared publication - Debasmita Saha
University of Gour Banga
1 shared publication - Saroj Kumar Biswas
Computer Science and Engineering Department, National Institute of Technology Silchar, Silchar, India
1 shared publication - Suvradip Sinha
Gour Mahavidyalaya,Department of Comp. Sc.,Malda,West Bengal,India
1 shared publication
Latest Publications
- 22 August 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-8
CRES-SMVI: Credit Risk Expert System Based on Selective Missing Value Imputation Using Machine Learning Analytics
Missing value imputation is one of the major problems for any data-driven model. Credit risk prediction isn't any exception either. However, the selection of appropriate missing value imputation techniques for credit default prediction remains an underexplored area in existing literature. The paper introduces ‘CRES-SMVI,' a ‘Credit Risk Expert System based on Selective Missing Value Imputation,' developed using ensemble learning techniques. The model integrates advanced preprocessing techniques, including SMOTE for handling imbalanced data and seven missing value imputation methods for effective comparison. By employing multiple classifiers such as Decision Tree (DT), Catboost (CB), and Extra Trees (ET), the system aimed to provide accurate credit default predictions. The study evaluated twenty-one models in total and finally found the best among them. As per the study, Multivariate imputation by chained equations (MICE)-based ET model performed better than all the other models, irrespective of the datasets.
- 22 August 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-8
CRES-SMVI: Credit Risk Expert System Based on Selective Missing Value Imputation Using Machine Learning Analytics
Missing value imputation is one of the major problems for any data-driven model. Credit risk prediction isn't any exception either. However, the selection of appropriate missing value imputation techniques for credit default prediction remains an underexplored area in existing literature. The paper introduces ‘CRES-SMVI,' a ‘Credit Risk Expert System based on Selective Missing Value Imputation,' developed using ensemble learning techniques. The model integrates advanced preprocessing techniques, including SMOTE for handling imbalanced data and seven missing value imputation methods for effective comparison. By employing multiple classifiers such as Decision Tree (DT), Catboost (CB), and Extra Trees (ET), the system aimed to provide accurate credit default predictions. The study evaluated twenty-one models in total and finally found the best among them. As per the study, Multivariate imputation by chained equations (MICE)-based ET model performed better than all the other models, irrespective of the datasets.