计算机科学 ›› 2019, Vol. 46 ›› Issue (12): 8-12.doi: 10.11896/jsjkx.180901813

• 大数据与数据科学 • 上一篇    下一篇

AdaBoostRS:高维不平衡数据学习的集成整合

杨平安, 林亚平, 祝团飞   

  1. (湖南大学信息科学与工程学院 长沙410000)
  • 收稿日期:2018-09-27 出版日期:2019-12-15 发布日期:2019-12-17
  • 通讯作者: 林亚平(1956-),男,博士,教授,博士生导师,主要研究方向为计算机网络、云安全和机器学习等,E-mail:yplin@hun.edu.cn。
  • 作者简介:杨平安(1995-),女,硕士生,主要研究方向为机器学习、数据挖掘等,E-mail:ypingan@hnu.edu.cn;祝团飞(1987-),男,博士,CCF会员,主要研究方向为云安全和机器学习等。

AdaBoostRS:Integration of High-dimensional Unbalanced Data Learning

YANG Ping-an, LIN Ya-ping, ZHU Tuan-fei   

  1. (College of Information Science and Engineering,Hunan University,Changsha 410000,China)
  • Received:2018-09-27 Online:2019-12-15 Published:2019-12-17

摘要: 机器学习中类不平衡分布问题包含了不同类之间数据样本的偏差分布,导致学习过程更偏向于多数类。而高维数据的稀疏性使得分类的偏差更加明显,因此对于高维不平衡数据,维度灾难与类不平衡分布这两个挑战性问题相互叠加在一起,使得解决高维不平衡问题变得更为困难。针对这一问题,文中提出结合随机子空间和SMOTE过采样技术的AdaBoost集成方法(AdaBoost ensemble of Random subspace and SMOTE,AdaBoostRS)来处理高维不平衡数据的分类。具体地,AdaBoostRS通过随机子空间选取部分特征来训练每个分类器,以增加分类样本的多样性和降低高维数据的维度,然后通过SMOTE方法对降维数据的少数类进行线性插值,以解决类不平衡问题。基于8个高维不平衡的标准时间序列数据集进行实验,结果表明,以F-measure、G-mean与AUC 3个性能指标来进行评判,AdaBoostRS优于传统的集成学习方法。

关键词: AdaBoost, SMOTE, 高维不平衡, 随机子空间

Abstract: The class imbalance problem in machine learning contains a skewed distribution of data samples among different classes,resulting in a learning bias toward the majority class.In high-dimensional data,the sparseness of the data makes the classification bias more obvious.For high-dimensional unbalanced data,the two challenging problems of dimensional disaster and class imbalance distribution are superimposed,making it more difficult to solve high-dimensional imbalance problems.This paper proposed an AdaBoost integration method combining random subspace and SMOTE oversampling technology,named AdaBoostRS (AdaBoost ensemble of Random subspace and SMOTE),to deal with the classification of high-dimensional unbalanced data.AdaBoostRS trains each classifier by selecting partial features in a random subspace to increase the diversity of the classification samples and reduce the dimensions of the high-dimensional data.Thena few classes of dimensionality reduction data are linearly interpolated through the SMOTE method to solve the class imbalance problem.The experiment is based on 8 high-dimensional unbalanced standard time series dataset.The results show that AdaBoostRS is superior to the traditional integrated learning method in terms of three performance indicators of F-measure,G-mean and AUC.

Key words: AdaBoost, High-dimensional imbalance, Random subspace, SMOTE

中图分类号: 

  • TP301.6
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