太阳成8722(中国)有限公司-GREEN NO.1

标题: A random forest classifier with cost-sensitive learning to extract urban landmarks from an imbalanced dataset

作者: Kang, MJ (Kang, Mengjun); Liu, Y (Liu, Yue); Wang, MQ (Wang, Mengqi); Li, L (Li, Lin); Weng, M (Weng, Min)

来源出版物: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE

DOI: 10.1080/13658816.2021.1977814

摘要: Urban landmarks play an important role as spatial references in spatial cognition, navigation, map design and urban planning. However, the current landmark extraction methods do not consider the imbalance between the landmark and non-landmarknon-landmark samples in a dataset, so the extraction results are biased toward the class with the majority of sample data, resulting in poor classification performance for the class with the fewest sample data. This study introduces a random forest (RF) classifier combined with cost-sensitive learning to extract urban landmarks automatically from a basic spatial database. First, the optimal feature set is determined according to the importance of features. Next, a cost-sensitive RF algorithm is applied to extract landmarks, which determines the misclassification cost according to the class distribution, and each decision tree is weighted by the classification results. The method has good performance, with a recall and area under the ROC curve (AUC) greater than 90%, and the model is also applicable to small sample sets, which can reduce the cost of manual labor.

作者关键词: Urban landmark; salience; random forest; class imbalance; cost-sensitive ensemble

地址: [Kang, Mengjun; Liu, Yue; Wang, Mengqi; Li, Lin; Weng, Min] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

通讯作者地址: Weng, M (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

电子邮件地址: wengmin@whu.edu.cn

影响因子：4.186