Improving Risk Predictions by Preprocessing Imbalanced Credit Data
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Otros documentos de la autoría: García, Vicente; Marqués Marzal, Ana Isabel; Sánchez Garreta, Josep Salvador
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/54899
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Título
Improving Risk Predictions by Preprocessing Imbalanced Credit DataFecha de publicación
2012Editor
Springer Berlin HeidelbergISBN
978-3-642-34480-0ISSN
0302-9743; 1611-3349Cita bibliográfica
García, Vicente ; Marqués, Ana Isabel; Sánchez, José Salvador. "Improving Risk Predictions by Preprocessing Imbalanced Credit Data". En: Neural Information Processing – 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part II / Huang, Tingwen [et al.] (Eds.). Berlin : Springer, 2012. (Lecture Notes in Computer Science; 7664) . ISBN: 978-3-642-34480-0, pp. 68-75Tipo de documento
info:eu-repo/semantics/bookPartVersión de la editorial
http://link.springer.com/chapter/10.1007%2F978-3-642-34481-7_9Palabras clave / Materias
Resumen
Imbalanced credit data sets refer to databases in which the class of defaulters is heavily under-represented in comparison to the class of non-defaulters. This is a very common situation in real-life credit scoring ... [+]
Imbalanced credit data sets refer to databases in which the class of defaulters is heavily under-represented in comparison to the class of non-defaulters. This is a very common situation in real-life credit scoring applications, but it has still received little attention. This paper investigates whether data resampling can be used to improve the performance of learners built from imbalanced credit data sets, and whether the effectiveness of resampling is related to the type of classifier. Experimental results demonstrate that learning with the resampled sets consistently outperforms the use of the original imbalanced credit data, independently of the classifier used. [-]
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