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|Title:||Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis||Authors:||Li, Shukai
Tsang, Ivor Wai-Hung
Chaudhari, Narendra Shivaji
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2011||Series/Report no.:||Expert systems with applications||Abstract:||In this paper, a relevance vector machine based infinite decision agent ensemble learning (RVMIdeal) system is proposed for the robust credit risk analysis. In the first level of our model, we adopt soft margin boosting to overcome overfitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron Kernel is employed in RVM to simulate infinite subagents. Our system RVMIdeal also shares some good properties, such as good generalization performance, immunity to overfitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy.||URI:||https://hdl.handle.net/10356/97655
|DOI:||10.1016/j.eswa.2011.10.022||Rights:||© 2011 Elsevier Ltd.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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