Improving Risk Predictions by Preprocessing Imbalanced Credit Data
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Other documents of the author: García, Vicente; Marqués Marzal, Ana Isabel; Sánchez Garreta, Josep Salvador
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/54899
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Title
Improving Risk Predictions by Preprocessing Imbalanced Credit DataDate
2012Publisher
Springer Berlin HeidelbergISBN
978-3-642-34480-0ISSN
0302-9743; 1611-3349Bibliographic citation
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. 68-75Type
info:eu-repo/semantics/bookPartPublisher version
http://link.springer.com/chapter/10.1007%2F978-3-642-34481-7_9Subject
Abstract
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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