基于聚类混合采样的不平衡数据分类

doi:10.3969/j.issn.1006-2475.2020.05.006

计算机与现代化 ›› 2020, Vol. 0 ›› Issue (05): 34-.doi: 10.3969/j.issn.1006-2475.2020.05.006

基于聚类混合采样的不平衡数据分类

(华南理工大学数学学院,广东广州510641)

收稿日期:2019-09-17 出版日期:2020-05-20 发布日期:2020-05-21
作者简介:史明华(1995-),女,河南商丘人,硕士研究生,研究方向:机器学习,数据挖掘,E-mail: 1043135305@qq.com; 吴广潮(1972-),男,广东潮阳人,副教授,硕士生导师,博士，研究方向:机器学习,数据挖掘，E-mail: magchwu@scut.edu.cn。

An Imbalanced Data Classification of Hybrid Sampling Based on Clustering

(School of Mathematics, South China University of Technology, Guangzhou 510641, China)

Received:2019-09-17 Online:2020-05-20 Published:2020-05-21

摘要/Abstract

摘要： 不平衡分类问题广泛地应用于现实生活中，针对大多数重采样算法侧重于类间平衡，较少关注类内数据分布不平衡问题，提出一种基于聚类的混合采样算法。首先对原始数据集聚类，然后对每一簇样本计算不平衡比，根据不平衡比的大小对该簇样本做出相应处理，最后将平衡后的数据集放入GBDT分类器进行训练。实验表明该算法与几种传统算法相比F1-value和AUC更高，分类效果更好。

关键词: 不平衡数据, 聚类；混合采样, GBDT

Abstract: The imbalanced classification problem is widely used in real life. For most resampling algorithms, it focuses on the balance between classes and pays less attention to the problem of data distribution imbalance within classes, a hybrid sampling algorithm based on clustering is proposed. Firstly, the original data set is clustered, then the imbalance ratio is calculated for each cluster sample, and the cluster sample is processed according to the imbalance ratio. Finally, the balanced data set is put into the GBDT classifier for training. Experiments show that the algorithm has higher F1-value, AUC and better classification results than several traditional algorithms.

Key words: imbalanced data, clustering, hybrid sampling, GBDT

中图分类号:

TP391

史明华,吴广潮. 基于聚类混合采样的不平衡数据分类[J]. 计算机与现代化, 2020, 0(05): 34-.

SHI Ming-hua, WU Guang-chao. An Imbalanced Data Classification of Hybrid Sampling Based on Clustering[J]. Computer and Modernization, 2020, 0(05): 34-.

参考文献

［1］ ZHU C M, WANG Z. Entropy-based matrix learning machine for imbalanced data sets［J］. Pattern Recognition Letters, 2017,88:72-80.
［2］ ZONG W W, HUANG G B, CHEN Y Q. Weighted extreme learning machine for imbalance learning［J］. Neurocomputing, 2013,101:229-242.
［3］ LIU Y H, CHEN Y T. Face recognition using total margin-based adaptive fuzzy support vector machines［J］. IEEE Transactions on Neural Networks, 2007,18(1):178-192.
［4］刘鹏,杜佳枝,吕伟刚,等. 面向不平衡数据集的一种改进的k-近邻分类器［J］. 东北大学学报(自然科学版), 2019,40(7):932-936.〖HJ1.1mm〗
［5］ YANG Z, TANG W H, SHINTEMIROV A, et al. Association rule mining-based dissolved gas analysis for fault diagnosis of power transformers［J］. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2009,39(6):597-610.
［6］ SUN Y M, WONG A K C, KAMEL M S. Classification of imbalanced data: A review［J］. International Journal of Pattern Recognition and Artificial Intelligence, 2009,34(4):687-719.
［7］ CHENG F Y, ZHANG J, WEN C H, et al. Large cost-sensitive margin distribution machine for imbalanced data classification［J］. Neurocomputing, 2017,224:45-57.
［8］ CASTRO C L, BRAGA A P. Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data［J］. IEEE Transactions on Neural Networks and Learning Systems, 2013,24(6):888-899.
［9］杨健健,唐至威,王晓林,等. 单类学习下基于VSAPSO-BP的掘进机异常检测方法［J］. 振动、测试与诊断, 2019,39(1):130-135.
［10］李勇,刘战东,张海军. 不平衡数据的集成分类算法综述［J］. 计算机应用研究, 2014,31(5):1287-1291.
［11］曹雅茜,黄海燕. 基于概率采样和集成学习的不平衡数据分类算法［J］. 计算机科学, 2019,46(5):203-208.
［12］CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic minority over-sampling technique［J］. Journal of Artificial Intelligence Research, 2002,16(1):321-357.
［13］HE H B, BAI Y, GARCIA E A, et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning［C］// Proceedings of the 2018 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). 2008:1322-1328.
［14］SEIFFERT C, KHOSHGOFTAAR T M, VAN HULSE J. Hybrid sampling for imbalanced data［C］// Proceedings of the 2008 IEEE International Conference on Information Reuse and Integration. 2008:202-207.
［15］GAZZAH S, HECHKEL A, BEN AMARA N E. A hybrid sampling method for imbalanced data［C］// Proceedings of the 2015 IEEE 12th International Multi-Conference on Systems, Signals and Devices. 2015, DOI: 10.1109/SSD.2015.7348093.
［16］张雨金,杨凌帆,葛双冶,等. 基于Kmeans-SVM的短期光伏发电功率预测［J］. 电力系统保护与控制, 2018,46(21):118-124.
［17］FRIEDMAN J H. Greedy function approximation: A gradient boosting machine［J］. The Annals of Statistics, 2001,29(5):1189-1232.
［18］FRIEDMAN J H. Stochastic gradient boosting［J］. Computational Statistics and Data Analysis, 2002,38(4):367-378.
［19］BLAKE C L, KEOGH E, MERZ C J. UCI Repository of Machine Learning Databases［DB/OL］. ［2019-06-17］. ftp://ftp.ics.uci.edu/pub/machine-learning-databases.
［20］周志华. 机器学习［M］. 北京:清华大学出版社, 2016:33-35.

[1]	周传华1, 2, 任太娇1, 罗岚1, 周昊1. 基于联合熵的非平衡数据边界混合重采样[J]. 计算机与现代化, 2024, 0(09): 95-100.
[2]	薛浩, 马静, 郭小宇. 基于Focal Loss改进LightGBM的供水管网毛刺数据检测[J]. 计算机与现代化, 2024, 0(09): 74-81.
[3]	刘付谦, 秦华妮, 赖惠慧. 基于SMOTE和贝叶斯优化的Adj-LightGBM人岗匹配算法[J]. 计算机与现代化, 2023, 0(03): 90-95.
[4]	孙丹, 施炜利, 饶兰香, 孟莎莎, 郭晓明, 李逸伦. 基于改进混合采样和XGBoost算法的信用卡欺诈检测方法[J]. 计算机与现代化, 2022, 0(09): 111-118.
[5]	周传华, 朱俊杰, 徐文倩, 邓佳佳. 基于聚类欠采样的集成分类算法[J]. 计算机与现代化, 2021, 0(11): 72-76.
[6]	董燕辉, 肖军弼, 张红霞, 杨勇进, 计志滨. 面向不平衡数据集的应用系统识别方法[J]. 计算机与现代化, 2021, 0(05): 93-97.
[7]	闫芮铵, 张立臣. 基于Focal Loss和卷积神经网络的入侵检测[J]. 计算机与现代化, 2021, 0(01): 65-69.
[8]	易未1,毛力1,孙俊1,吴林海2,3. 改进Smote算法在不平衡数据集上的分类研究[J]. 计算机与现代化, 2018, 0(03): 83-.
[9]	霍旭1，吴涛1,2. 对于不平衡数据的模糊时间序列预测[J]. 计算机与现代化, 2017, 0(12): 108-110.

基于聚类混合采样的不平衡数据分类

An Imbalanced Data Classification of Hybrid Sampling Based on Clustering

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价