A multi-objective approach for profit-driven feature selection in credit scoring
In credit scoring, feature selection aims at removing irrelevant data to improve the performance and interpretability of the scorecard. Standard techniques treat feature selection as a single-objective task and rely on statistical criteria such as correlation. Recent studies…
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- 2026-10-10
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Data mining feature selection for credit scoring models link.springer.com
The features used may have an important effect on the performance of credit scoring models. The process of choosing the best set of features for credit scoring models is usually unsystematic and dominated by somewhat arbitrary trial. This paper presents an empirical study of…
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Combination of feature selection approaches with SVM in credit scoring sciencedirect.com
An effective classificatory model in credit scoring will objectively help managers who rely on intuitive experience. This study proposes four approaches using the SVM (support vector machine) classifier for feature selection that retain sufficient information for classification…
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Classification methods applied to credit scoring: Systematic review and overall comparison sciencedirect.com
The need for controlling and effectively managing credit risk has led financial institutions to excel in improving techniques designed for this purpose, resulting in the development of various quantitative models by financial institutions and consulting companies. Hence, the…
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Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring ieeexplore.ieee.org
Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example of cost-sensitive classification. However, it is usually treated using methods that do…
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Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research sciencedirect.com
There have been several advancements in scorecard development, including novel learning methods, performance measures and techniques to reliably compare different classifiers, which the credit scoring literature does not reflect. This paper compares several novel classification…
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A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers sciencedirect.com
Surveys the techniques used — both statistical and operational research based — to help organisations decide whether or not to grant credit to consumers. It also discusses the need to incorporate economic conditions into the scoring systems and the way the systems could change…
Credit Modeling › Feature Selection: “In credit scoring, feature selection aims at removing irrelevant data to improve the performance and interpretability of the scorecard. Standard techniques treat feature selection as a single-objective task and rely on statistical criteria such as correlation. Recent studies…”