第4期 刘金平，等：面向不均衡数据的融合谱聚类的自适应过采样法 ·739· [12]ZHOU P,HU X,LI P,et al.Online feature selecton for ics-based adaptive synthetic sampling approach[J/OL]. high dimensional class-imbalanced data[J].Knowledge- Control and decision:https://doi.org/10.13195/j.kzyjc.2019. based systems,2017,136(15:187-199 1672. [13]QIAN Y,LIANG Y,LI M,et al.A resampling ensemble [23]CHAUHAN V K.DAHIYA K.SHARMA A.Problem- algorithm for classification of imbalance problems[J]. formulations and solvers in linear SVM:a review[J.Arti- Neurocomputing,2014,143(2):57-67. ficial intelligence review,2018.6(1):1-53. [14]LIU M.XU C,LUO Y,et al.Cost-sensitive feature selec- [24]PAUL A,MUKHERJEE D P,DAS P,et al.Improved tion by optimizing F-Measures[J].IEEE transactions on random forest for classification[J].IEEE transactionson image processing,2018,27(3):1323-35. image processing,2018,27(8):4012-24. [15]吴雨茜，王俊丽，杨丽，等.代价敏感深度学习方法研究 [25]ZHANG S,DENG Z,CHENG D,et al.Efficient KNN 综述.计算机科学，2019,46(5少：8-19. classification algorithm for big data[J].Neurocomputing, WU Yuqian,WANG Junli,YANG Li,et al.Survey on 2016,195(26):143-8. cost-sensitive deep learning methods[J].Computer sci- [26]林智勇，郝志峰，杨晓伟.若干评价准则对不平衡数据 ence,2019,46(5):8-19. 学习的影响凹.华南理工大学学报（自然科学版），2010 [16]HE H,BAI Y,GARCIA E A,et al.ADASYN:Adaptive 38(4):147-155. synthetic sampling approach for imbalanced learning[Cl// LIN Zhiyong,HAO Zhifeng,YANG Xiaowei.The influ- Neural Networks.Hong Kong,China,2008,3641-46 ence of several evaluation criteria on unbalanced data [17]AHMAD J,JAVED F,HAYAT M.Intelligent computa- learning[J].Journal of South China University of Techno- tional model for classification of sub-Golgi protein using logy (natural science edition),2010,38(4):147-155. oversampling and fisher feature selection methods[J.Ar- [27]THARWAT A.Classification assessment methods[J].Ap- tificial intelligence in medicine,2017,78(1):14-16. plied computing and informatics,2018,12(1):1-13. [18]LIN WC,TSAI C F,HU Y H,et al.Clustering-based un- 作者简介： dersampling in class-imbalanced data[J].Information sci- 刘金平，副教授，博士，主要研究 ences,2017,17(2):409-410. 方向为智能信息处理。 [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们]蔡晓妍，戴冠中，杨黎斌.谱聚类算法综述[.计算机科 学，2008(7)：14-18. 周嘉铭，硕士研究生，主要研究方 CAI Xiaoyan,DAI Guanzhong,YANG Libin.Survey on 向为数据挖掘、模式识别。 spectral clustering algorithms[J].Computer science, 2008(7:14-18. [21]NG A Y,JORDAN M I.WEISS Y.On spectral cluster- ing:Analysis and an algorithm[C]//Proceedings of the Advances in Neural Information Processing Systems. Berkeley,USA,2002:26-34. 贺俊宾，硕士研究生，主要研究方 [22]刘金平，周嘉铭，刘先锋，等.基于聚类簇结构特性的自 向为模式识别、计算机视觉。 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/10.1n3195j.kzyjc.2019.1672. LIU Jinping,ZHOU Jiaming,LIU Xianfeng,et al.To- ward intrusion detection via cluster-structure characterist-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. [12] QIAN Y, LIANG Y, LI M, et al. A resampling ensemble algorithm for classification of imbalance problems[J]. Neurocomputing, 2014, 143(2): 57–67. [13] LIU M, XU C, LUO Y, et al. Cost-sensitive feature selec￾tion by optimizing F-Measures[J]. IEEE transactions on image processing, 2018, 27(3): 1323–35. [14] 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 sci￾ence, 2019, 46(5): 8–19. [15] 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 [16] AHMAD J, JAVED F, HAYAT M. Intelligent computa￾tional model for classification of sub-Golgi protein using oversampling and fisher feature selection methods[J]. Ar￾tificial intelligence in medicine, 2017, 78(1): 14–16. [17] LIN W C, TSAI C F, HU Y H, et al. Clustering-based un￾dersampling in class-imbalanced data[J]. Information sci￾ences, 2017, 17(2): 409–410. [18] 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. [19] 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. [20] NG A Y, JORDAN M I, WEISS Y. On spectral cluster￾ing: Analysis and an algorithm[C]//Proceedings of the Advances in Neural Information Processing Systems. Berkeley, USA, 2002: 26−34. [21] 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.To￾ward intrusion detection via cluster-structure characterist- [22] ics-based adaptive synthetic sampling approach[J/OL]. Control and decision: https://doi.org/10.13195/j.kzyjc.2019. 1672. CHAUHAN V K, DAHIYA K, SHARMA A. Problem￾formulations and solvers in linear SVM: a review[J]. Arti￾ficial intelligence review, 2018, 6(1): 1–53. [23] PAUL A, MUKHERJEE D P, DAS P, et al. Improved random forest for classification[J]. IEEE transactionson image processing, 2018, 27(8): 4012–24. [24] ZHANG S, DENG Z, CHENG D, et al. Efficient KNN classification algorithm for big data[J]. Neurocomputing, 2016, 195(26): 143–8. [25] 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 influ￾ence of several evaluation criteria on unbalanced data learning[J]. Journal of South China University of Techno￾logy (natural science edition), 2010, 38(4): 147–155. [26] THARWAT A. Classification assessment methods[J]. Ap￾plied computing and informatics, 2018, 12(1): 1–13. [27] 作者简介： 刘金平，副教授，博士，主要研究 方向为智能信息处理。 周嘉铭，硕士研究生，主要研究方 向为数据挖掘、模式识别。 贺俊宾，硕士研究生，主要研究方 向为模式识别、计算机视觉。 第 4 期 刘金平，等：面向不均衡数据的融合谱聚类的自适应过采样法 ·739·