A Stacked Generalization Framework for City Traffic Related Geospatial Data Analysis

摘要：

　　Analyzing traffic related geospatial data often lacks in priori knowledge and encounters parameter setting problems due to the dynamic characteristics of city traffic.In this paper,we propose a pervasive,scalable framework for city traffic related geospatial data analysis based on a stacked generalization.Firstly we analyze the optimal linear combination based on stepwise iteration,and also prove its theoretical validity via error-ambiguity decomposition.Secondly we integrate six classical approaches into this framework,including linear least squares regression,autoregressive moving average,historical mean,artificial neural network,radical basis function neural network,support vector machine,and conduct experiments with a real city traffic detecting dataset.We further compare the proposed framework with other four linear combination models.It suggests that the proposed framework behaves more robust than other models both in variance and bias,showing a promising direction for city traffic related geospatial data analysis.

关键词： City traffic Geospatial data Ensemble learning Stacked generalization Robustness

作者: Xiliang Liu Li Yu Peng Peng Feng Lu

作者单位: State Key Laboratory of Resources and Environmental Information System,IGSNRR,Chinese Academy of Sci State Key Laboratory of Resources and Environmental Information System,IGSNRR,Chinese Academy of Sci State Key Laboratory of Resources and Environmental Information System,IGSNRR,Chinese Academy of Sci

会议类型: 国际会议

会议名称: International Asia-Pacific Web Conference(第18届国际亚太互联网大会)

会议地点: 苏州

会议语种:英文

页码: 265-276

在线出版日期: 2016-09-23（万方平台首次上网日期，不代表论文的发表时间）