Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97655
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
http://hdl.handle.net/10220/11127
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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