A Stacking Ensemble Learning Method to Classify the Patterns of Complex Road Junctions

被引:8
|
作者
Yang, Min [1 ]
Cheng, Lingya [1 ]
Cao, Minjun [1 ]
Yan, Xiongfeng [2 ]
机构
[1] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China
[2] Tongji Univ, Coll Surveying & Geoinformat, 1239 Siping Rd, Shanghai 200092, Peoples R China
基金
中国国家自然科学基金;
关键词
complex road junction; pattern classification; convolutional neural network; stacking ensemble; area coverage; CLASSIFICATION; NETWORK;
D O I
10.3390/ijgi11100523
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recognizing the patterns of road junctions in a road network plays a crucial role in various applications. Owing to the diversity and complexity of morphologies of road junctions, traditional methods that rely heavily on manual settings of features and rules are often problematic. In recent years, several studies have employed convolutional neural networks (CNNs) to classify complex junctions. These methods usually convert vector-based junctions into raster representations with a predefined sampling area coverage. However, a fixed sampling area coverage cannot ensure the integrity and clarity of each junction, which inevitably leads to misclassification. To overcome this drawback, this study proposes a stacking ensemble learning method for classifying the patterns of complex road junctions. In this method, each junction is first converted into raster images with multiple area coverages. Subsequently, several CNN-based base-classifiers are trained using raster images, and they output the probabilities of the junction belonging to different patterns. Finally, a meta-classifier based on random forest is used to combine the outputs of the base-classifiers and learn to arrive at the final classification. Experimental results show that the proposed method can improve the classification accuracy for complex road junctions compared to existing CNN-based classifiers that are trained using raster representations of junctions with a fixed sampling area coverage.
引用
收藏
页数:15
相关论文
共 50 条
  • [21] A prediction model for water absorption in sublayers based on stacking ensemble learning method
    Xiong, Wenjun
    Xiao, Lizhi
    Han, Dakuang
    Yue, Wenzheng
    GEOENERGY SCIENCE AND ENGINEERING, 2024, 239
  • [22] The Rotate Stress of Steam Turbine prediction method based on Stacking Ensemble Learning
    Liang, Haoran
    Song, Lei
    Li, Xuzhi
    201919TH IEEE INTERNATIONAL SYMPOSIUM ON HIGH ASSURANCE SYSTEMS ENGINEERING (HASE 2019), 2019, : 146 - 149
  • [23] Boosted Stacking Ensemble Machine Learning Method for Wafer Map Pattern Classification
    Choi, Jeonghoon
    Suh, Dongjun
    Otto, Marc-Oliver
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (02): : 2945 - 2966
  • [24] A machine learning method based on stacking heterogeneous ensemble learning for prediction of indoor humidity of greenhouse
    Melal, Sepehr Rezaei
    Aminian, Mahdi
    Shekarian, Seyed Mohammadhossein
    JOURNAL OF AGRICULTURE AND FOOD RESEARCH, 2024, 16
  • [25] Link Prediction in Complex Networks Using Recursive Feature Elimination and Stacking Ensemble Learning
    Wang, Tao
    Jiao, Mengyu
    Wang, Xiaoxia
    ENTROPY, 2022, 24 (08)
  • [26] Enhanced stacking ensemble Model: A statistical ensemble pruning framework to classify anxiety severity for responsive emergency preparedness
    Anitha, G.
    Manickam, J. Martin Leo
    Mohan, Surapaneni Krishna
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2024, 87
  • [27] Identifying Complex Junctions in a Road Network
    Yang, Jianting
    Zhao, Kongyang
    Li, Muzi
    Xu, Zhu
    Li, Zhilin
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (01)
  • [28] A Stacking Ensemble Learning Framework for Genomic Prediction
    Liang, Mang
    Chang, Tianpeng
    An, Bingxing
    Duan, Xinghai
    Du, Lili
    Wang, Xiaoqiao
    Miao, Jian
    Xu, Lingyang
    Gao, Xue
    Zhang, Lupei
    Li, Junya
    Gao, Huijiang
    FRONTIERS IN GENETICS, 2021, 12
  • [29] Forecasting Heart Disease Risk with a Stacking-Based Ensemble Machine Learning Method
    Wu, Yuanyuan
    Xia, Zhuomin
    Feng, Zikai
    Huang, Mengxing
    Liu, Huizhou
    Zhang, Yu
    ELECTRONICS, 2024, 13 (20)