Interval-valued data prediction via regularized artificial neural network

NEUROCOMPUTING

卷:331

Article

Yang, Zebin¹  Lin, Dennis K. J.²  Zhang, Aijun¹ 

[1] Univ Hong Kong, Dept Stat & Actuarial Sci, Pokfulam Rd, Hong Kong, Peoples R China

[2] Penn State Univ, Dept Stat, University Pk, PA 16802 USA

关键词: Interval-valued data;    Non-crossing regularization;    Artificial neural network;    Backpropagation;   

DOI  :  10.1016/j.neucom.2018.11.063

来源: Elsevier

【 摘 要 】

The prediction of interval-valued data is a challenging task as the predicted lower bounds of intervals should not cross over the corresponding upper bounds. In this paper, a regularized artificial neural network (RANN) is proposed to address this difficult problem. It provides a flexible trade-off between prediction accuracy and interval crossing. Compared to existing hard-constrained methods, the RANN has the advantage that it does not necessarily reduce the prediction accuracy while preventing interval crossing. Extensive experiments are conducted based on both simulation and real-life datasets, with comparison to multiple traditional models, including the linear constrained center and range method, the least absolute shrinkage and selection operator-based interval-valued regression, the nonlinear interval kernel regression, the interval multi-layer perceptron and the multi-output support vector regression. Experimental results show that the proposed RANN model is an effective tool for interval-valued data prediction tasks with high prediction accuracy. (C) 2018 Elsevier B.V. All rights reserved.

【 授权许可】

Free