(2018) Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing, International Journal of Geographical Information Science, DOI: 10.1080/13658816.2018.1530355
博文
科研小喵：一篇关于超参数空间优化的文章
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最近刚上线的一篇发表在GIS领域国际权威期刊IJGIS的文章，创新性地开展了基于并行计算开展的超参数空间优研究。对超参数进行优化已经有很多研究，而从这个新角度开展超参数优化好像还是首次。
附件：
Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing
Minrui Zheng,Wenwu Tang &Xiang Zhao
ABSTRACT
Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling.
KEYWORDS: Artificial neural network, hyperparameter optimization, spatially explicit modeling, high-performance computing, spatial statistics
Author information
Minrui Zheng
Minrui Zheng is a Ph.D. candidate at Department of Geography and Earth Sciences, University of North Carolina at Charlotte. Her research interests include spatial analysis and modeling, cyberinfrastructure and high-performance computing, and machine learning algorithms.
Wenwu Tang
Wenwu Tang is an associate professor at Department of Geography and Earth Sciences, University of North Carolina at Charlotte. His research interests include spatial analysis and modeling, agent-based models and spatiotemporal simulation, cyberinfrastructure and high-performance computing, complex adaptive spatial systems, and land use and land cover change.
Xiang Zhao
Xiang Zhao is a faculty of School of resource and environmental sciences, Wuhan University. His research interest includes land use planning, agent-based modeling and cyberinfrastructure and high performance computing.
To cite this article:
论文链接：https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1530355
https://blog.sciencenet.cn/blog-43347-1141613.html
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