On the use of data filtering techniques for credit risk prediction with instance-based models
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Other documents of the author: García, Vicente; Sánchez Garreta, Josep Salvador; Marqués Marzal, Ana Isabel
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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http://dx.doi.org/10.1016/j.eswa.2012.05.075 |
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Title
On the use of data filtering techniques for credit risk prediction with instance-based modelsDate
2012Publisher
ElsevierISSN
0957-4174Type
info:eu-repo/semantics/articlePublisher version
http://ac.els-cdn.com/S0957417412007919/1-s2.0-S0957417412007919-main.pdf?_tid=d ...Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
Many techniques have been proposed for credit risk prediction, from statistical models to artificial intelligence methods. However, very few research efforts have been devoted to deal with the presence of noise and ... [+]
Many techniques have been proposed for credit risk prediction, from statistical models to artificial intelligence methods. However, very few research efforts have been devoted to deal with the presence of noise and outliers in the training set, which may strongly affect the performance of the prediction model. Accordingly, the aim of the present paper is to systematically investigate whether the application of filtering algorithms leads to an increase in accuracy of instance-based classifiers in the context of credit risk assessment. The experimental results with 20 different algorithms and 8 credit databases show that the filtered sets perform significantly better than the non-preprocessed training sets when using the nearest neighbour decision rule. The experiments also allow to identify which techniques are most robust and accurate when confronted with noisy credit data. [-]
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Expert Systems with Applications Volume 39, Issue 18, 15 December 2012Rights
© 2012 Elsevier Ltd. All rights reserved.
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