标题：An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing作者：Yang, Yi; Liu, Jiping; Xu, Shenghua; Zhao, Yangyang
来源出版物：ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 卷：5 期：1 DOI：10.3390/ijgi5010004 出版年：JAN 2016
摘要：This paper proposes an extended semi-supervised regression approach to enhance the prediction accuracy of housing prices within the geographical information science field. The method, referred to as co-training geographical weighted regression (COGWR), aims to fully utilize the positive aspects of both the geographical weighted regression (GWR) method and the semi-supervised learning paradigm. Housing prices in Beijing are assessed to validate the feasibility of the proposed model. The COGWR model demonstrated a better goodness-of-fit than the GWR when housing price data were limited because a COGWR is able to effectively absorb no-price data with explanatory variables into its learning by considering spatial variations and nonstationarity that may introduce significant biases into housing prices. This result demonstrates that a semisupervised geographic weighted regression may be effectively used to predict housing prices.
入藏号：WOS:000369606900003
文献类型：Article
语种：English
作者关键词：semi-supervised regression, geographical weighted regression, spatial nonstationarity, housing prices
扩展关键词：SPATIAL NONSTATIONARITY; HETEROGENEITY; MARKETS; MODEL; SEGMENTATION; TESTS; LAND
通讯作者地址：Yang, Y (reprint author), Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.
Xu, SH (reprint author), Chinese Acad Surveying & Mapping, Res Ctr Govt GIS, 28 Lianhuachi West Rd, Beijing 100830, Peoples R China.
电子邮件地址：yangyilyg@126.com; liujp@casm.ac.cn; xushh@casm.ac.cn; nhyyangyang@126.com
地址：
[Yang, Yi] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.
[Liu, Jiping; Xu, Shenghua; Zhao, Yangyang] Chinese Acad Surveying & Mapping, Res Ctr Govt GIS, 28 Lianhuachi West Rd, Beijing 100830, Peoples R China.
研究方向：Physical Geography; Remote Sensing
ISSN：2220-9964
