基于ECOC平衡随机森林的雷达降水粒子分类

doi:10.12305/j.issn.1001-506X.2024.05.14

Abstract

Abstract:

To address the problem of hydrometeor classification with data imbalance condition, this paper proposes a hydrometeor classification method based on error correcting output code (ECOC) balanced random forest for dual-polarization weather radar. Firstly, the multiclass hydrometeor dataset is coded into multiple binary datasets, and then the binary datasets are balanced resampling with replacement to construct multiple classification and regression trees. Finally, all the classification and regression trees are used to jointly classify hydrometeors. The processing results of the measured data indicate that the proposed method can significantly improve the classification effect of minority classes while ensuring a high overall accuracy.

Key words: dual-polarization weather radar, hydrometeor classification, data imbalance, error correcting output code (ECOC), balanced random forest

CLC Number:

959.4

Hai LI, Zhong TIAN, Jun QIAN. Hydrometeor classification for radar based on ECOC-balanced random forest[J]. Systems Engineering and Electronics, 2024, 46(5): 1599-1606.

Figures/Tables 11

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References 31

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超参数类别	超参数初值
平衡随机森林数量M	(30, 35, 40, 45, 50)
每个平衡随机森林包含的CART树数量L	(10, 50, 100, 200, 300)
CART树每次分裂抽取的特征数量P	(1, 2, 3)

类标签	类别
C=0	冰晶
C=1	雪
C=2	雨
C=3	霰
C=4	冰雹

类别	数据集
类别	1	2	3	4	…	28	29	30
冰晶	0	1	1	1	…	1	0	1
雪	0	0	1	0	…	0	1	0
雨	1	1	0	0	…	1	0	1
霰	1	1	1	1	…	0	1	0
冰雹	0	0	1	1	…	1	0	1

	粒子个数	粒子占比/%
冰晶	3 080	2.51
雪	27 754	22.61
雨	86 613	70.56
霰	2 458	2.00
冰雹	2 847	2.32

真实情况(真实值)	分类结果(预测值)
真实情况(真实值)	正例	反例
正例	真正例(true positive, TP)	假反例(false negative, FN)
反例	假正例(false positive, FP)	真反例(true negative, TN)

Hydrometeor classification for radar based on ECOC-balanced random forest

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算法	准确率	F1度量	平均召回率
SVM	79.29	52.12	46.56
欠采样SVM	53.75	41.81	73.95
ECOC平衡随机森林	74.00	59.50	78.78

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