A regularized artificial neural network (RANN) is proposed for interval-valued data prediction. The ANN model is selected due to its powerful capability in fitting linear and nonlinear functions. To meet mathematical coherence requirement for an interval (i.e., the predicted lower bounds should not cross over their upper bounds), a soft non-crossing regularizer is introduced to the interval-valued ANN model. We conduct extensive experiments based on both simulation datasets and real-life datasets, and compare the proposed RANN method with multiple traditional models, including the linear constrained center and range method (CCRM), the least absolute shrinkage and selection operator-based interval-valued regression method (Lasso-IR), the nonlinear interval kernel regression (IKR), the interval multi-layer perceptron (iMLP) and the multi-output support vector regression (MSVR). Experimental results show that the proposed RANN model is an effective tool for interval-valued prediction tasks with high prediction accuracy.
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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