Journal
FLEXIBLE SERVICES AND MANUFACTURING JOURNAL
Volume 28, Issue 4, Pages 576-592Publisher
SPRINGER
DOI: 10.1007/s10696-015-9226-2
Keywords
Credit risk assessment; Deep belief network; Ensemble learning; Extreme learning machine
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Funding
- National Science Fund for Distinguished Young Scholars (NSFC) [71025005]
- National Natural Science Foundation of China (NSFC) [71433001, 91224001, 71301006]
- National Program for Support of Top-Notch Young Professionals
- Fundamental Research Funds for the Central Universities in BUCT
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To achieve high assessment accuracy for credit risk, a novel multistage deep belief network (DBN) based extreme learning machine (ELM) ensemble learning methodology is proposed. In the proposed methodology, three main stages, i.e., training subsets generation, individual classifiers training and final ensemble output, are involved. In the first stage, bagging sampling algorithm is applied to generate different training subsets for guaranteeing enough training data. Second, the ELM, an effective AI forecasting tool with the unique merits of time-saving and high accuracy, is utilized as the individual classifier, and diverse ensemble members can be accordingly formulated with different subsets and different initial conditions. In the final stage, the individual results are fused into final classification output via the DBN model with sufficient hidden layers, which can effectively capture the valuable information hidden in ensemble members. For illustration and verification, the experimental study on one publicly available credit risk dataset is conducted, and the results show the superiority of the proposed multistage DBN-based ELM ensemble learning paradigm in terms of high classification accuracy.
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