An Oversampling Method for Imbalance Data Based on Three-Way Decision Model

HU Feng; WANG Lei; ZHOU Yao

doi:10.3969/j.issn.0372-2112.2018.01.019

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An Oversampling Method for Imbalance Data Based on Three-Way Decision Model

更新时间：2025-07-16

- An Oversampling Method for Imbalance Data Based on Three-Way Decision Model
- Acta Electronica Sinica Vol. 46, Issue 1, Pages: 135-144(2018)
- 作者机构：
  
  计算智能重庆市重点实验室(重庆邮电大学),重庆,400065
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61309014, No.61379114, No.61472056);Humanities and Social Science Program of Ministry of Education (No.15XJA630003);Chongqing Research Program of Basic and Frontier Technology (No.cstc2013jcyjA40063, No.cstc2014jcyjA40049);Science and Technology Research of Chongqing Municipal Education Commission (No.KJ1500416)
- DOI：10.3969/j.issn.0372-2112.2018.01.019
  CLC： TP39
- Published Online：25 January 2018，
  
  Published：2018
- 稿件说明：
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HU Feng, WANG Lei, ZHOU Yao. An Oversampling Method for Imbalance Data Based on Three-Way Decision Model[J]. Acta Electronica Sinica, 2018, 46(1): 135-144.
DOI：

HU Feng, WANG Lei, ZHOU Yao. An Oversampling Method for Imbalance Data Based on Three-Way Decision Model[J]. Acta Electronica Sinica, 2018, 46(1): 135-144. DOI： 10.3969/j.issn.0372-2112.2018.01.019.

摘要

采样是解决不平衡数据分类问题的一个有效途径.文中结合三支决策理论，根据样本分布将样本划分成三个区域：正域、边界域和负域；在此基础上，分别对边界域和负域中的小类样本进行不同的过采样处理，提出了一种基于三支决策的不平衡数据过采样算法（TWD-IDOS算法）.实验结果表明，在C4.5、KNN和CART等分类器上，文中提出的算法能有效解决不平衡数据的二分类问题，在Recall、F-value、AUC等指标上优于文献中的过采样算法.

Abstract

Sampling is an effective way to solve the problem of unbalanced data classification. According to the distribution of samples

we employ the three-way decision model to divide the universe into three parts:positive region

boundary region and negative region. After that

we oversample the minority class samples in boundary region and negative region respectively. Then

a novel oversampling algorithm for imbalance data based on three-way decision model

namely TWD-IDOS

is developed. The experimental results show that the proposed method can effectively solve the two-class classification problems of imbalanced data and has a better performance in such measures (Recall

F-value

AUC) on C45

KNN and CART classifiers than other oversampling methods.

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