不平衡数据加权边界点集成欠采样方法

doi:10.19665/j.issn1001-2400.2021.04.023

Abstract

Abstract:

In order to effectively solve the problem that boundary points are deleted directly from unbalanced data and effectively maintain the information on most kinds of data,a clustering-based weighted boundary point integration undersampling algorithm is proposed.First,the algorithm extracts the number of minority class sets as the initial number of clustering centers of majority class sets to cluster.Then,the variation coefficient is introduced to identify the boundary points,and the identified boundary points are weighted so that the weighted boundary points can be added to the unbalanced data processing.Then,the cluster density is used to divide majority class sets into the high-density cluster and low-density cluster,delete the low-density cluster,and finally obtain the reduced majority of the sample sets.Then,the reduced majority of class samples is combined with the minority of class samples to form a balanced data set,which is trained with the Ada boost to get the final classification model.This method can be used to reduce the dataset and improve the efficiency of execution.The results show that the proposed method can effectively handle the problem of unbalanced data,and improve the execution efficiency and accuracy of the under-sampling algorithm for unbalanced data weighted boundary point integration.

Key words: sampling, clustering, unbalanced data, weighted boundary point

CLC Number:

TP311.13

HE Yunbin,LENG Xin,WAN Jing. Unbalanced data weighted boundary point integration undersampling method[J].Journal of Xidian University, 2021, 48(4): 176-183.

Figures/Tables 6

References 16

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数据集	样本数量	少数类数据集	多数类数据集	不平衡率/%
Glass	214	29	185	6.38
Vehicle	846	199	647	3.25
Satimage	6 435	626	5 809	9.28
Vowel	990	90	900	10.00
Pima	768	268	500	1.87
Haberman	306	81	225	2.78
Letter	20 000	734	19 266	26.25

数据集	C4.5	SMOTE-Boost	PC-Boost	Ada-Boost	CWBUSC
Glass	0.785	0.846	0.978	0.813	0.981
Vehicle	0.879	0.617	0.862	0.554	0.901
Satimage	0.564	0.678	0.657	0.647	0.784
Vowel	0.937	0.955	0.975	0.971	0.991
Pima	0.600	0.648	0.519	0.681	0.702
Haberman	0.323	0.391	0.407	0.369	0.423
Letter	0.947	0.986	0.969	0.691	0.951

数据集	C4.5	SMOTE-Boost	PC-Boost	Ada-Boost	CWBUSC
Glass	0.859	0.911	0.949	0.894	0.950
Vehicle	0.925	0.953	0.962	0.955	0.977
Satimage	0.727	0.760	0.822	0.770	0.853
Vowel	0.958	0.987	0.921	0.976	0.989
Pima	0.661	0.724	0.767	0.703	0.789
Haberman	0.486	0.556	0.674	0.523	0.680
Letter	0.937	0.975	0.958	0.848	0.948

Unbalanced data weighted boundary point integration undersampling method

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