Acta Geodaetica et Cartographica Sinica >

2017 , Vol. 46 >Issue 1: 123 - 129

DOI: https://doi.org/10.11947/j.AGCS.2017.20150470

A Geographic Weighted Regression Method Based on Semi-supervised Learning

  • ZHAO Yangyang ,
  • LIU Jiping ,
  • XU Shenghua ,
  • ZHANG Fuhao ,
  • YANG Yi
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  • 1. School of Mapping and Geographical Science, Liaoning Technical University, Fuxin 123000, China;
    2. Chinese Academy of Surveying and Mapping, Beijing 100830, China

Received date: 2015-09-14

  Revised date: 2016-07-11

  Online published: 2017-02-06

Supported by

The Special Scientific Research Fund of Public Welfare Profession of China (No.201512032);The National Key Research and Development Program of China(No.2016YFC0803101)

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Abstract

Geographically weighted regression (GWR) approach will be affected by the quantity of label data. However, it is difficult to get labeled data but easy to get the unlabeled data in applications. Therefore it is indispensable to find an useful way that can use the unlabeled data to improve the regression results. As we know semi-supervised learning is a class of supervised learning tasks and techniques that also make use of unlabeled data for training typically a small amount of labeled data with a large amount of unlabeled data. So this article develops a semi-supervised-learning geographically weighted regression (SSLGWR). Firstly it builds the GWR model by labeled data. Then the unlabeled data can be calculated the value by the GWR model and they will be signed as new labeled data. Thirdly, use both labeled data and new labeled data to rebuild the GWR model to improve the model's precision. The experiments use both simulated data and real data to compare GWR COGWR and SSLGWR. Mean square error is closed as the framework to estimate the models. Experiments using simulated data have shown that the proposed model improves the performance by 39.66%, 11.92% and 0.94% relative to 10%,30% and 50% label data. And experiments using real data have shown that the proposed model improves the performance by 8.94%, 3.36% and 5.87%. The results demonstrate that there are substantial benefits of SSLGWR in the improvement of GWR.

Key words： geographically weighted regression; semi-supervised learning; SSLGWR; population distribution

Cite this article

ZHAO Yangyang , LIU Jiping , XU Shenghua , ZHANG Fuhao , YANG Yi . A Geographic Weighted Regression Method Based on Semi-supervised Learning[J]. Acta Geodaetica et Cartographica Sinica, 2017 , 46(1) : 123 -129 . DOI: 10.11947/j.AGCS.2017.20150470

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