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用于城市增长建模的三种新型成本敏感型机器学习模型

IF 3.3 4区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Geocarto International Pub Date : 2024-05-20 DOI:10.1080/10106049.2024.2353252
Mohammad Ahmadlou, Mohammad Karimi, Saad Sh. Sammen, Karam Alsafadi
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引用次数: 0

摘要

本文针对伊朗大不里士和伊斯法罕特大城市城市增益建模（UGM）中的类不平衡问题，提出了新颖的成本敏感型机器学习模型，即成本敏感型机器学习模型。
本文章由计算机程序翻译，如有差异，请以英文原文为准。
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Three novel cost-sensitive machine learning models for urban growth modelling
This article addresses the class imbalance problem in urban gain modelling (UGM) of Tabriz and Isfahan megacities in Iran by proposing novel cost-sensitive machine learning models, namely cost-sens...
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来源期刊
Geocarto International
Geocarto International ENVIRONMENTAL SCIENCES-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
6.30
自引率
13.20%
发文量
407
审稿时长
>12 weeks
期刊介绍： Geocarto International is a professional academic journal serving the world-wide scientific and user community in the fields of remote sensing, GIS, geoscience and environmental sciences. The journal is designed: to promote multidisciplinary research in and application of remote sensing and GIS in geosciences and environmental sciences; to enhance international exchange of information on new developments and applications in the field of remote sensing and GIS and related disciplines; to foster interest in and understanding of science and applications on remote sensing and GIS technologies; and to encourage the publication of timely papers and research results on remote sensing and GIS applications in geosciences and environmental sciences from the world-wide science community. The journal welcomes contributions on the following: precise, illustrated papers on new developments, technologies and applications of remote sensing; research results in remote sensing, GISciences and related disciplines; Reports on new and innovative applications and projects in these areas; and assessment and evaluation of new remote sensing and GIS equipment, software and hardware.
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