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- W2387485587 abstract "To solve the low classification accuracy and poor interpretability of selected features in traditional credit risk evaluation, a new model using Sparse Bayesian Learning( SBL) to evaluate personal credit risk( SBLCredit) was proposed in this paper. The SBLCredit utilized the advantages of SBL to get as sparse as possible solutions under the priori knowledge on the weight of features, which led to both good classification performance and effective feature selection. SBLCredit improved the classification accuracy of 4. 52%, 6. 40%, 6. 26% and 2. 27% averagely when compared with the state-of-the-art KNearest Neighbour( KNN), Nave Bayes, decision tree and support vector machine respectively on real-world German and Australian credit datasets. The experimental results demonstrate that the proposed SBLCredit is a promising method for credit risk evaluation with higher accuracy and fewer features." @default.
- W2387485587 created "2016-06-24" @default.
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- W2387485587 date "2013-01-01" @default.
- W2387485587 modified "2023-09-26" @default.
- W2387485587 title "Sparse Bayesian learning for credit risk evaluation" @default.
- W2387485587 hasPublicationYear "2013" @default.
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