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

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

Sebastián Maldonado2017

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Articlegreen2017

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

Sebastián Maldonado · Juan Pérez · Cristián Bravo

155 citations·0/0 files stored·European Journal of Operational Research

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Financial Distress and Bankruptcy Prediction Statistical Methods and Inference Text and Document Classification Technologies

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References

2002Credit Scoring and Its Applications 2007The Credit Scoring Toolkit 2014Development and application of consumer credit scoring models using profit-based classification measures

Cited by

2019A multi-objective approach for profit-driven feature selection in credit scoring 2018A novel multi-stage hybrid model with enhanced multi-population niche genetic algorithm: An application in credit scoring 2020A study on credit scoring modeling with different feature selection and machine learning approaches 2021A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance 2017Integrated framework for profit-based feature selection and SVM classification in credit scoring 2022A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain 2021Combining feature selection, instance selection, and ensemble classification techniques for improved financial distress prediction 2024Credit Risk Prediction Using Machine Learning and Deep Learning: A Study on Credit Card Customers

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Details

Year2017

Typearticle

Published2017-02-27

VenueEuropean Journal of Operational Research

ISSN-L0377-2217

Languageen

Citations155

Files0 public / 0

ShelfFinancial Distress and Bankruptcy Prediction

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doi10.1016/j.ejor.2017.02.037

openalex_workW2593370983

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