A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data
Abstract
Credit risk assessment is often accompanied with sampling data imbalance. For this reason, this paper tries to propose a deep belief network (DBN) based resampling support vector machine (SVM) ensemble learning paradigm to solve imbalanced data problem in credit classification. In this paradigm, a bagging algorithm is first used to generate variable training subsets to make the subsets rebalanced and suitable in size. Then the SVM model is used...
Paper Details
Title
A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data
Published Date
Aug 1, 2018
Journal
Volume
69
Pages
192 - 202
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