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An experimental comparison of classification algorithms for imbalanced credit scoring data sets

Expert systems with applications, 2012-02, Vol.39 (3), p.3446-3453

2012

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An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

Decision Support Systems, 2011-04, Vol.51 (1), p.141-154

2011

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How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments

European journal of operational research, 2021-10, Vol.294 (2), p.711-722

2021

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Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation

Management science, 2003-03, Vol.49 (3), p.312-329

2003

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An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market

European journal of operational research, 2016-03, Vol.249 (2), p.427-439

2016

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Mining software repositories for comprehensible software fault prediction models

The Journal of systems and software, 2008-05, Vol.81 (5), p.823-839

2008

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The value of text for small business default prediction: A Deep Learning approach

European journal of operational research, 2021-12, Vol.295 (2), p.758-771

2021

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A transformer-based model for default prediction in mid-cap corporate markets

European journal of operational research, 2023-07, Vol.308 (1), p.306-320

2023

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Building comprehensible customer churn prediction models with advanced rule induction techniques

Expert systems with applications, 2011-03, Vol.38 (3), p.2354-2364

2011

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Attention-based dynamic multilayer graph neural networks for loan default prediction

European journal of operational research, 2024-09

2024

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Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data

International journal of forecasting, 2012-01, Vol.28 (1), p.183-195

2012

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Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

IEEE transactions on software engineering, 2008-07, Vol.34 (4), p.485-496

2008

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Benchmarking regression algorithms for loss given default modeling

International journal of forecasting, 2012-01, Vol.28 (1), p.161-170

2012

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Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction

ISPRS journal of photogrammetry and remote sensing, 2022-05, Vol.187, p.378-392

2022

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Modelling credit card exposure at default using vine copula quantile regression

European journal of operational research, 2023-11, Vol.311 (1), p.387-399

2023

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Editorial

The Journal of the Operational Research Society, 2022-01, Vol.73 (1), p.1-4

2022

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Editorial

The Journal of the Operational Research Society, 2022-01, Vol.73 (1), p.1-4

2022

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A mixture model for credit card exposure at default using the GAMLSS framework

International journal of forecasting, 2023-01, Vol.39 (1), p.503-518

2023

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Learning algorithm selection for comprehensible regression analysis using datasetoids

Intelligent data analysis, 2015-09, Vol.19 (5), p.1019-1034

2015

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Decision diagrams in machine learning: an empirical study on real-life credit-risk data

Expert systems with applications, 2004-08, Vol.27 (2), p.257-264

2004

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Editorial

European journal of operational research, 2016-03, Vol.249 (2), p.395-396

2016

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Recursive Neural Network Rule Extraction for Data With Mixed Attributes

IEEE transactions on neural networks, 2008-02, Vol.19 (2), p.299-307

2008

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Call for papers special issue on credit risk modelling

The Journal of the Operational Research Society, 2019-05, Vol.70 (5), p.ii-ii

2019

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Debtor level collection operations using Bayesian dynamic programming

The Journal of the Operational Research Society, 2019-08, Vol.70 (8), p.1332-1348

2019

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Mixture cure models in credit scoring: If and when borrowers default

European journal of operational research, 2012-04, Vol.218 (1), p.132-139

2012

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An experimental comparison of classification algorithms for imbalanced credit scoring data sets

An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments

Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation

An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market

Mining software repositories for comprehensible software fault prediction models

The value of text for small business default prediction: A Deep Learning approach

A transformer-based model for default prediction in mid-cap corporate markets

Building comprehensible customer churn prediction models with advanced rule induction techniques

Attention-based dynamic multilayer graph neural networks for loan default prediction

Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data

Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

Benchmarking regression algorithms for loss given default modeling

Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction

Modelling credit card exposure at default using vine copula quantile regression

Editorial

Editorial

A mixture model for credit card exposure at default using the GAMLSS framework

Learning algorithm selection for comprehensible regression analysis using datasetoids

Decision diagrams in machine learning: an empirical study on real-life credit-risk data

Editorial

Recursive Neural Network Rule Extraction for Data With Mixed Attributes

Call for papers special issue on credit risk modelling

Debtor level collection operations using Bayesian dynamic programming

Mixture cure models in credit scoring: If and when borrowers default

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