计算机科学 ›› 2017, Vol. 44 ›› Issue (4): 275-280.doi: 10.11896/j.issn.1002-137X.2017.04.057
邢胜,王晓兰,赵士欣,赵彦霞
XING Sheng, WANG Xiao-lan, ZHAO Shi-xin and ZHAO Yan-xia
摘要： 针对加权极速学习机人为固定权重可能会错失更优权重的问题,提出了改进的加权极速学习机。该方法的多数类的初始权重设为1,使用多数类与少数类样例数的比值作为少数类的初始权重,然后通过在多数类或者少数类中添加权重调节因子,从缩小和扩大两个方向去调节权重,最后通过实验结果选出最优的权重。实验分别使用原加权极速学习机、其他权重的极速学习机和新方法在改造的UCI数据集上进行比较。结果表明新方法无论是在F-mea-sure还是G-mean上都要优于其他加权极速学习机。
| [1] THOMAS C.Improving intrusion detection for imbalanced network traffic[J].Security & Communication Networks,2013,6(6):309-324. [2] DROSOU K,GEORGIOU S,KOUKOUVINOS C,et al.Support vector machines classification on class imbalanced data:a case study with real medical data[J].Journal of Data Science,2014,12(4):143-155. [3] FAWCETT T.“In vivo” spam filtering:a challenge problem for data mining[J].SIGKDD Explorations,2003,5(2):140-148. [4] ZHANG C X,ZHANG J S.A Survey of Selective EnsembleLearning Algorithms[J].Chinese Journal of Computers,2011,34(8):1399-1410.(in Chinese) 张春霞,张讲社.选择性集成学习算法综述[J].计算机学报,2011,34(8):1399-1410. [5] TOMEK I.Two modifications of CNN[J].IEEE Transactionson Systems,Man and Communications,1976,6(11):769-772. [6] CHAWLA N V,BOWYER K W,HALLL O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research,2002,16(1):321-357. [7] SUN Y,WONG A K C,KAMEL M S.Classification of imba-lanced data:a review[J].International Journal of Pattern Recognition and Artificial Intelligence,2009,23(4):687-719. [8] ETHERIDGE H L,SRIRAM R S,HSU H Y K.A comparison of selected artificial neural networks that help auditors evaluate client financial viability[J].Decision Sciences,2007,31(2):531-550. [9] FU Z L.Cost-sensitive AdaBoost Algorithm for Multi-classClassification Problems[J].Acta Automatica Sinica,2011,37(8):973-983.(in Chinese) 付忠良.多分类问题代价敏感AdaBoost算法[J].自动化学报,2012,37(8):973-983 [10] MASNADI H,VASCONCELOS N,IRANMEHR A.Cost-sensitive support vector machines[J].Journal of Machine Learning Research,2015,1(1):1-26. [11] ZONG W W,HUANG G B,CHEN Y.Weighted extreme lear-ning machine for imbalance learning[J].Neurocomputing,2013,101(3):229-242. [12] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning ma- chine:theory and applications[J].Neurocomputing,2006,70(1-3):489-501. [13] HUANG G B,WANG D H,LAN Y.Extreme learning machines:a survey[J].International Journal of Machine Learning &Cybernetics,2011,2(2):107-122. [14] XU Y G,DENG W K,CHEN L D.Online fault diagnosis inwastewater treatment process by kernel-based weighted extreme learning machine[J].CIESC Journal,2016,67(9):3817-3825.(in Chinese) 许玉格,邓文凯,陈立定.基于核函数的加权极限学习机污水处理在线故障诊断[J].化工学报,2016,67(9):3817-3825. [15] YU B J,ZHU Y L.Transformer fault diagnosis using weighted extreme learning machine[J].Computer Engineering & Design,2013,34(12):4340-4344.(in Chinese) 遇炳杰,朱永利.加权极限学习机在变压器故障诊断中的应用[J].计算机工程与设计,2013,34(12):4340-4344. [16] GAO X,CHEN Z,TANG S,et al.Adaptive weighted imbalance learning with application to abnormal activity recognition[J].Neurocomputing,2015,173:1927-1935. [17] YANG Z P.Neural Network Based Classification Methods for Imbalanced Datasets[D].Shanghai:South China University of Technology,2015.(in Chinese) 杨泽平.基于神经网络的不平衡数据分类方法研究[D].上海:华东理工大学,2015. [18] DASKALAKI S,KOPANAS I,AVOURIS N.Evaluation ofclassifiers for an uneven class distribution problem[J].Applied Artificial Intelligence,2006,20(5):381-417. [19] KUBAT M,MATWIN S.Addressing the curse of imbalanced training sets:one-sided selection[C]∥Proceedings of the Fourteenth International Conference on Machine Learning.2000:179-186 |
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