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：ISPRS International Journal of Geo-Information, 2016, 5(1): 4.

DOI：10.3390/ijgi5010004

• semi-supervised regression
• geographical weighted regression
• spatial nonstationarity
• housing prices

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.

• 出版日期2016-1
• 单位武汉大学; 中国测绘科学研究院