L 0 -Norm Variable Adaptive Selection for Geographically Weighted Regression Model
Bibliographic Data
| ID | 7634935 |
|---|---|
| Authors | Bo Wu (0000-0002-8911-1625, Jiangxi Normal University), Jinbiao Yan (0000-0003-4523-5818, Hengyang Normal University), Kai Cao (0000-0002-8043-1462, East China Normal University) |
| Year | 2023 |
| Volume | 113 |
| Issue | 5 |
| Pages | 1190-1206 |
| Publication date | 2023-05-28 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the American Association of Geographers (JOURNAL) |
| Journal identifiers | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2022.2161988 |
| OpenAlex | W4322763381 |
| Language | EN |
| Citations received | 3 |
| References cited | 33 |
A geographically weighted regression (GWR) model with fewer explanatory variables and higher prediction accuracy is required in spatial analysis and other practical applications. This article proposes an l0-norm variable adaptive selection method to enhance performances of a GWR by simultaneously performing model selection and coefficient optimization. Specifically, we formulate a regularized GWR model with an additional l0-norm constraint to shrink those unimportant regression coefficients toward zero and propose an adaptive variable selection algorithm by iteratively distinguishing the important variables from the variable set. At each location, the best variable subset and optimizing coefficient estimations are simultaneously achieved under the l0-GWR framework. Moreover, two novel criteria, the modified Bayesian information criterion and the interpretability of coefficient symbol, which specify the variable selection and model interpretation, respectively, are also introduced to improve the performance of the l0-GWR. Experiments on both simulated and actual data sets demonstrate that the proposed algorithm can significantly improve the estimation accuracy of coefficients and can also enhance the interpretative ability of the established model
Algorithm · Data mining · Feature selection · Interpretability · Mathematical optimization · Regression · Regression analysis · Statistics · Computer Science · Land Use and Ecosystem Services · Mathematics · Regional Economics and Spatial Analysis · Spatial and Panel Data Analysis · Artificial Intelligence
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| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |