[1]LIU Jinping,ZHOU Jiaming,HE Junbin,et al.Spectral clustering-fused adaptive synthetic oversampling approach for imbalanced data processing[J].CAAI Transactions on Intelligent Systems,2020,15(4):732-739.[doi:10.11992/tis.201909062]
Copy

Spectral clustering-fused adaptive synthetic oversampling approach for imbalanced data processing

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 4 Page number: 732-739 Column: 学术论文—机器学习 Public date: 2020-07-05
Title:
Spectral clustering-fused adaptive synthetic oversampling approach for imbalanced data processing
Author(s):
LIU Jinping1;  ZHOU Jiaming1;  HE Junbin1; 2;  TANG Zhaohui3;  XU Pengfei1;  ZHANG Guoyong3
1. Hu’nan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hu’nan Normal University, Changsha 410081, China;
2. Hu’nan Institute of Metrology and Test, Changsha 410014, China;
3. School of Automation, Central South University, Changsha 410082, China
Keywords:
adaptive synthetic sampling approach (ADASYN); imbalanced data se-t; spectral clustering; oversampling; pattern classification; data distribution; biased classifier; data pre-processing
CLC:
TP391
DOI:
10.11992/tis.201909062
Abstract:
Classification is a research hotspot in the field of machine learning. Most classic classifiers assume that the distribution of dataset is generally balanced, while the data se-t in reality often has a problem of class imbalance. Namely, the number of data belonging to the normal/majority category and the amount of anomaly/minority data vary greatly. If the data is not processed, the classifier will ignore the minority and be biased towards the majority, which deteriorates the classification results. Focusing on the problem of data imbalance, this paper proposes a spectral clustering-fused comprehensive sampling algorithm (SCF-ADASYN). First, the spectral clustering method is employed to analyze the distribution information of the minority-type samples in the imbalanced dataset, and the samples of minority class are oversampled to obtain a relatively balanced dataset, used for the classification model training. A large number of experiments have been carried out on multiple unbalanced datasets. The results show that the SCF-ADASYN can effectively improve the imbalance on the data se-t, and the classification accuracies of the testing classifiers on the unbalanced data se-t can be significantly improved.
References:
[1] LESSMANN S, BAESENS B, SEOW H V, et al. Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research[J]. European journal of operational research, 2015, 247(1): 124-36.
[2] LIU J, HE J, ZHANG W, et al. TCvBsISM: Texture classification via B-splines-based image statistical modeling[J]. IEEE access, 2018, 6(1): 76-93.
[3] 翟云, 杨炳儒, 曲武. 不平衡类数据挖掘研究综述[J]. 计算机科学, 2010, 37(10): 27-32
ZHAI Yun, YANG Bingru, QU Wu. Survey of mining imbalanced datasets[J]. Computer science, 2010, 37(10): 27-32
[4] LIN W C, TSAI C F, HU Y H, et al. Clustering-based undersampling in class-imbalanced data[J]. Information sciences, 2017, 17(2): 409-410.
[5] HE H, GARCIA E A. Learning from imbalanced data[J]. IEEE transactions on knowledge & data engineering, 2009, 21(9): 1263-84.
[6] LIU J, TANG Z, ZHANG J, et al. Visual perception-based statistical modeling of complex grain image for product quality monitoring and supervision on assembly production line[J]. Plos one, 2016, 11(3): 1-25.
[7] 刘天羽, 李国正, 尤鸣宇. 不均衡故障诊断数据上的特征选择[J]. 小型微型计算机系统, 2009, 30(5): 924-927
LIU Tianyu, LI Guozheng, YOU Mingyu. Feature selection on unbalanced fault diagnosis data[J]. Journal of Chinese computer systems, 2009, 30(5): 924-927
[8] YUAN X, XIE L, ABOUELENIEN M. A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data[J]. Pattern recognition, 2018, 77(1): 160-72.
[9] LIU J, HE J, ZHANG W, et al. ANID-SEoKELM: adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features[J]. Knowledge-based systems, 2019, 177(1): 104-16.
[10] 刘金平, 张五霞, 唐朝晖, 等. 基于模糊粗糙集属性约简与GMM-LDA最优聚类簇特征学习的自适应网络入侵检测[J]. 控制与决策, 2019, 34(2): 243-251
LIU Jinping, ZHANG Wuxia, TANG Zhaohui, et al. Adaptive network intrusion detection based on fuzzy rough set-based attribute reduction and GMM-LDA-based optimal cluster feature learning[J]. Control and decision, 2019, 34(2): 243-251
[11] FOTOUHI S, ASADI S, KATTAN M W. A comprehensive data level analysis for cancer diagnosis on imbalanced data[J]. Journal of biomedical informatics, 2018, 90(1): 1-29.
[12] ZHOU P, HU X, LI P, et al. Online feature selecton for high dimensional class-imbalanced data[J]. Knowledge-based systems, 2017, 136(15): 187-199.
[13] QIAN Y, LIANG Y, LI M, et al. A resampling ensemble algorithm for classification of imbalance problems[J]. Neurocomputing, 2014, 143(2): 57-67.
[14] LIU M, XU C, LUO Y, et al. Cost-sensitive feature selection by optimizing F-Measures[J]. IEEE transactions on image processing, 2018, 27(3): 1323-35.
[15] 吴雨茜, 王俊丽, 杨丽, 等. 代价敏感深度学习方法研究综述[J]. 计算机科学, 2019, 46(5): 8-19
WU Yuqian, WANG Junli, YANG Li, et al. Survey on cost-sensitive deep learning methods[J]. Computer science, 2019, 46(5): 8-19
[16] HE H, BAI Y, GARCIA E A, et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning[C]// Neural Networks. Hong Kong, China, 2008, 3641-46
[17] AHMAD J, JAVED F, HAYAT M. Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods[J]. Artificial intelligence in medicine, 2017, 78(1): 14-16.
[18] LIN W C, TSAI C F, HU Y H, et al. Clustering-based undersampling in class-imbalanced data[J]. Information sciences, 2017, 17(2): 409-410.
[19] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of artificial intelligence research, 2011, 16(1): 321-357.
[20] 蔡晓妍, 戴冠中, 杨黎斌. 谱聚类算法综述[J]. 计算机科学, 2008(7): 14-18
CAI Xiaoyan, DAI Guanzhong, YANG Libin. Survey on spectral clustering algorithms[J]. Computer science, 2008(7): 14-18
[21] NG A Y, JORDAN M I, WEISS Y. On spectral clustering: Analysis and an algorithm[C]//Proceedings of the Advances in Neural Information Processing Systems. Berkeley, USA, 2002: 26-34.
[22] 刘金平, 周嘉铭, 刘先锋, 等. 基于聚类簇结构特性的自适应综合采样法在入侵检测中的应用[J/OL]. 控制与决策: https://doi.org/10.1n3195/j.kzyjc.2019.1672.
LIU Jinping, ZHOU Jiaming, LIU Xianfeng, et al.Toward intrusion detection via cluster-structure characteristics-based adaptive synthetic sampling approach[J/OL]. Control and decision: https://doi.org/10.13195/j.kzyjc.2019.1672.
[23] CHAUHAN V K, DAHIYA K, SHARMA A. Problemformulations and solvers in linear SVM: a review[J]. Artificial intelligence review, 2018, 6(1): 1-53.
[24] PAUL A, MUKHERJEE D P, DAS P, et al. Improved random forest for classification[J]. IEEE transactionson image processing, 2018, 27(8): 4012-24.
[25] ZHANG S, DENG Z, CHENG D, et al. Efficient KNN classification algorithm for big data[J]. Neurocomputing, 2016, 195(26): 143-8.
[26] 林智勇, 郝志峰, 杨晓伟. 若干评价准则对不平衡数据学习的影响[J]. 华南理工大学学报(自然科学版), 2010, 38(4): 147-155
LIN Zhiyong, HAO Zhifeng, YANG Xiaowei. The influence of several evaluation criteria on unbalanced data learning[J]. Journal of South China University of Technology (natural science edition), 2010, 38(4): 147-155
[27] THARWAT A. Classification assessment methods[J]. Applied computing and informatics, 2018, 12(1): 1-13.
Similar References:

Memo

-

Last Update: 2020-07-25

Copyright © CAAI Transactions on Intelligent Systems