基于包络学习和分级结构一致性机制的不平衡集成算法

李帆; 张小恒; 李勇明; 王品

doi:10.12263/DZXB.20220712

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基于包络学习和分级结构一致性机制的不平衡集成算法

学术论文 | 更新时间：2025-12-11

- 基于包络学习和分级结构一致性机制的不平衡集成算法
- Imbalanced Ensemble Algorithm Based on Envelope Learning and Hierarchical Structure Consistency Mechanism
- 电子学报 2024年52卷第3期页码：751-761
- 作者机构：
  
  1.重庆大学微电子与通信工程学院，重庆 400030
  2.重庆广播电视大学，重庆400052
- 作者简介：
  
  [ "李帆男， 1993年出生于湖北省. 博士研究生. 主要研究领域为非平衡数据处理、机器学习.E-mail: 979940181@qq.com" ]
  [ "张小恒男， 1980年生，四川达州人. 博士研究生，副教授. 主要研究领域为医学信号处理、机器学习.E-mail: 7818320@qq.com" ]
  [ "李勇明男， 1976年生于四川. 博士，教授，博士生导师. 主要研究领域为医学信号处理、机器学习.中国电子学会会员编号：E190020470M.E-mail: yongmingli@cqu.edu.cn" ]
  [ "王品女， 1979年出生于江苏省. 博士，副教授，硕士生导师. 主要研究领域为图像处理与识别.E-mail: wangpin@cqu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(61771080;U21A20448);中央高校基本科研业务费(2022CDJJJ-003)
- DOI：10.12263/DZXB.20220712
  中图分类号： TP391.4
- 收稿：2022-06-20，
  
  修回：2022-11-03，
  
  纸质出版：2024-03-25
- 稿件说明：
移动端阅览
李帆,张小恒,李勇明,等.基于包络学习和分级结构一致性机制的不平衡集成算法[J].电子学报,2024,52(03):751-761.

LI Fan, ZHANG Xiao-heng, LI Yong-ming, et al.Imbalanced Ensemble Algorithm Based on Envelope Learning and Hierarchical Structure Consistency Mechanism[J].Acta Electronica Sinica, 2024, 52(03): 751-761.
李帆,张小恒,李勇明,等.基于包络学习和分级结构一致性机制的不平衡集成算法[J].电子学报,2024,52(03):751-761. DOI：10.12263/DZXB.20220712

LI Fan, ZHANG Xiao-heng, LI Yong-ming, et al.Imbalanced Ensemble Algorithm Based on Envelope Learning and Hierarchical Structure Consistency Mechanism[J].Acta Electronica Sinica, 2024, 52(03): 751-761. DOI：10.12263/DZXB.20220712

摘要

集成方法是不平衡学习方法的重要分支，然而，现有不平衡集成方法均作用于原样本而没考虑样本的结构信息，因此其效能仍然有限.样本的结构信息包括局部和全局结构信息.为了解决上述问题，本文提出了一种基于深度样本包络网络（Deep Instance Envelope Network， DIEN）和分级结构一致性机制（Hierarchical Structure Consistency Mechanism， HSCM）的不平衡集成学习算法.该算法在考虑局部流形和全局结构信息的情况下，通过多层样本聚类，生成高质量的多层包络样本，从而实现类平衡化.首先，算法基于样本近邻拼接和模糊C均值聚类算法，设计DIEN来挖掘样本的结构信息，得到深度包络样本.然后，设计局部流形结构度量和全局结构分布度量来构建HSCM用于增强层间样本的分布一致性.接着，将DIEN和HSCM结合起来，构建出优化后的深度样本包络网络——DH （DIEN with HSCM）. 之后，将基分类器应用于包络样本.最后，设计bagging集成学习机制来融合基分类器的预测结果.文末组织了多组实验，采用了十多个公共数据集和有代表性的相关算法进行验证比较.实验结果表明，本文算法在AUC（Area Under Curve）， F-measure等四个性能指标上显著最优.本文完整实验结果与分析参见链接：

https://pan.baidu.com/s/15lZ9GztB95ySrNwEmtrCfA

，提取码：1111

Abstract

Ensemble methods have become an important branch of imbalanced learning. However

the existing imbalanced ensemble methods all rely on the original instances without considering the structure information of the instances

so their effectiveness is still limited. The research shows that the structure information of instances includes local and global structure information. In order to solve the above problem

this paper proposes an imbalanced ensemble algorithm based on deep instance envelope network (DIEN) and hierarchical structure consistency mechanism (HSCM). Considering the local manifold and global structure information

the algorithm generates high-quality deep envelope instances to achieve class balance. Firstly

based on the instance neighborhood concatenation and fuzzy c-means clustering algorithm

the DIEN is designed to mine the structure information of instances

obtaining the deep envelope instances. Then

the local manifold structure measure and global structure distribution measure are designed to construct the HSCM to enhance the distribution consistency of interlayer instances. Next

DIEN and HSCM are combined to construct the optimized deep instance envelope network—DH (DIEN with HSCM). Then

the base classifier is applied to the deep envelope instances. Finally

the bagging ensemble learning mechanism is designed to fuse the prediction results of the base classifier to obtain the final results. At the end of this paper

several groups of experiments are organized. More than 10 public datasets and representative related algorithms are used for verification. Experimental results show that the proposed algorithm is significantly better in four performance metrics

such as AUC (Area Under Curve) and F-measure.

关键词

Keywords

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