Name
Title | ||
|---|---|---|
An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.3390/ijgi5010004 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION |
Keywords | Field | DocType |
semi-supervised regression,geographical weighted regression,spatial nonstationarity,housing prices | Econometrics,Geographic information system,Regression,Unit-weighted regression,Co-training,Geography,Beijing | Journal |
Volume | Issue | Citations |
5 | 1 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Yang | 1 | 1 | 0.72 |
| Jiping Liu | 2 | 11 | 6.00 |
| Shenghua Xu | 3 | 0 | 0.34 |
| Yangyang Zhao | 4 | 52 | 9.46 |