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An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing

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School of Resource and Environmental Science, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
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Research Center of Government GIS, Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Haidian District, Beijing100830, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(1), 4; https://doi.org/10.3390/ijgi5010004
Received: 21 October 2015 / Revised: 25 December 2015 / Accepted: 31 December 2015 / Published: 12 January 2016
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. View Full-Text
Keywords: semi-supervised regression; geographical weighted regression; spatial nonstationarity; housing prices semi-supervised regression; geographical weighted regression; spatial nonstationarity; housing prices
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Yang, Y.; Liu, J.; Xu, S.; Zhao, Y. An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing. ISPRS Int. J. Geo-Inf. 2016, 5, 4.

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