计算机科学 ›› 2019, Vol. 46 ›› Issue (1): 94-99.doi: 10.11896/j.issn.1002-137X.2019.01.014
才子昕, 王馨月, 徐剑, 景丽萍
CAI Zi-xin, WANG Xin-yue, XU Jian, JING Li-ping
摘要: 大数据时代,不平衡数据分类在实际应用场景中频繁出现。以二分类为例,传统分类器由于较难学习少数类数据集内部的本质结构,容易将少数类样本错误分类。针对这一问题,一种有效的解决方法是在传统的方法中引入代价敏感机制,为少数类样本赋予更高的误分代价以提升其预测精度。这类方法同等对待了同类样本集中的数据,然而同一类内的不同样本可能对训练过程有不同程度的贡献。为了提升代价敏感机制的有效性,样本自适应的代价敏感策略为不同的样本赋予不同的权重。首先,通过考察样本局部的类分布情况,判断其距离两类样本边界的远近;然后,根据边界分布理论,即距离决策面越近的样本对决策面位置的影响越大,为距离两类样本边界越近的样本赋予越高的权重。实验过程中,通过将样本自适应代价敏感策略应用于LDM,并在标准数据集上进行一系列对比实验,验证了样本自适应代价敏感策略在处理不平衡数据分类问题上的有效性。
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[1]RADIVOJAC P,CHAWLA N V,DUNKER A K,et al.Classification and knowledge discovery in protein databases[J].Journal of Biomedical Informatics,2004,37(4):224-239.<br /> [2]ZOU Q,GUO M Z,LIU Y,et al.A classification method for class imbalanced data and its application on bioinformatics[J].Journal of Computer Research and Development,2010,47(8):1407-1414.(in Chinese)<br /> 邹权,郭茂祖,刘扬,等.类别不平衡的分类方法及在生物信息学中的应用[J].计算机研究与发展,2010,47(8):1407-1414.<br /> [3]EZAWA K J,SINGH M,NORTON S W.Learning goal oriented Bayesian networks for telecommunications risk management[C]//Proceedings of the International Conference on Machine Lear-ning.Bari,Italy:Morgan Kauffman,1996:139-147.<br /> [4]SANZ JA,BERNARDO D,HERRERA F,et al.A compact evolutionary interval valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data[C]//Proceedings of IEEE Trans on Fuzzy Systems,2015,23(4):973-990.<br /> [5]SU J S,ZHANG B F,XU X.Advances in machine learning based text categorization[J].Journal of Software,2006,17(9):1848-1859.(in Chinese)<br /> 苏金树,张博锋,徐昕.基于机器学习的文本分类技术研究进展[J].软件学报,2006,17(9):1848-1859.<br /> [6]DEEBA F,MOHAMMED S K,BUI F M,et al.Learning from imbalanced data:a comprehensive comparison of classifier performance for bleeding detection in endoscopic video[C]//Proceedings of International Conference on Informatics,Electronics and Vision.IEEE,2016:1006-1009.<br /> [7]RANI K U,RAMADEVI G N,LAVANYA D.Performance of synthetic minority oversampling technique on imbalanced breast cancer data[C]//Proceedings of International Conference on Computing for Sustainable Global Development.IEEE,2016:1623-1627.<br /> [8]PROVOST F.Machine learning from imbalanced data sets 101[C]//Proceedings of the AAAI’2000 Workshop on Imbalanced Data.IEEE,2000.<br /> [9]RAO R B.Data mining for improved cardiac care[J].ACM SIGKDD Explorations Newsletter,2006,8(1):3-10.<br /> [10]DOMINGOS P.MetaCost:A general method for making classifiers cost-sensitive[C]//Proceedings of Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mi-ning.San Diego:CA,ACM,1999:155-164.<br /> [11]VEROPOULOS K,CAMPBELL C,CRISTIANINI N.Controlling the sensitivity of support vector machines[C]//Proceedings of the International Joint Conference on Artificial Intelligence.Stockholm,Sweden,1999:55-60.<br /> [12]CHENG F Y,ZHANG J,WEN C H.Cost-sensitive large margin distribution machine for classification of imbalanced data[J].Pattern Recognition Letters,2016,80(C):107-112.<br /> [13]CORTES C,VAPNIK V.Support-vector networks[J].Machine Learning,1995,20(5):273-297.<br /> [14]STEFANOWSKI J.Dealing with data difficulty factors while learning from imbalanced data.http://www.cs.put.poznan.pl/jstefanowski/pub/jkbook7wersjaWWW.pdf.<br /> [15]CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:Synthetic minority oversampling technique[J].Journal of Artificial Intelligence Research,2002,16(1):321-357.<br /> [16]HAN H,WANG W Y,MAO B H.Borderline-SMOTE:A new over-sampling method in imbalanced data sets learning[C]//Proceedings of International Conference on Intelligent Computing.Springer-Verlag,2005:878-887.<br /> [17]HE H,BAI Y,GARCIA E A,et al.ADASYN:Adaptive synthetic sampling approach for imbalanced learning[C]//Procee-dings of IEEE International Joint Conference on Neural Networks.IEEE,2008:1322-1328.<br /> [18]TANG B,HE H.KernelADASYN:Kernel based adaptive synthetic data generation for imbalanced learning[C]//Proceedings of Evolutionary Computation.IEEE,2015:664-671.<br /> [19]BATISTA G,PRATI R C,MONARD M C.A study of the behavior of several methods for balancing machine learningtrai-ning data[J].ACM SIGKDD Explorations Newsletter,2004,6(1):20-29.<br /> [20]CIESLAK D A,CHAWLA N V,STRIEGEL A.Combating imbalance in network intrusion datasets[C]//Proceedings of IEEE International Conference on Granular Computing.IEEE,2006:732-737.<br /> [21]BATUWITA R,PALADE V.Efficient resampling methods for training support vector machines with imbalanced datasets[C]//Proceedings of International Joint Conference on Neural Networks.IEEE,2010:1-8.<br /> [22]ZHOU Z H,LIU X Y.Training cost-sensitive neural networks with methods addressing the class imbalance problem[J].IEEE Trans on Knowledge and Data Engineering,2006,18(1):63-77.<br /> [23]SUN Z,SONG Q,ZHU X,et al.A novel ensemble method for classifying imbalanced data[J].Pattern Recognition,2015,48(5):1623-1637.<br /> [24]CHEN C,BREIMAN L.Using random forest to learn imbalanced data:Technical Report 666 .Berkeley:Department of Statistics,UC Berkeley,2004.<br /> [25]CHAN P K,STOLFO S J.Toward scalable learning with nonuniform class and cost distributions:a case study in credit card fraud detection[C]//International Conference on Knowledge Discovery and Data Mining.AAAI,1998:164-168.<br /> [26]YOAV F,SCHAPIRE R E.A desicion-theoretic generalization of online learning and an application to boosting[C]//Procee-dings of European Conference on Computational Learning Theory.Heidelberg,Berlin:Springer,1995:23-37.<br /> [27]WANG B X,JAPKOWICZ N.Boosting support vector machines for imbalanced data sets[J].Knowledge and Information Systems,2010,25(1):1-20.<br /> [28]SEIFFERT C,KHOSHGOFTAAR T M,HULSE J V,et al. RUSBoost:A hybrid approach to alleviating class imbalance[J].IEEE Trans on Systems Man and Cybernetics Part A Systems and Humans,2010,40(1):185-197.<br /> [29]GALAR M,BARRENECHEA E,HERRERA F.EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary under-sampling[J].Pattern Recognition,2013,46(12):3460-3471.<br /> [30]LIU X Y,WU J X,ZHOU Z H.Exploratory under-sampling for class-imbalance learning[J].IEEE Trans on System,Man and Cybernetics B,2009,39(2):539-550.<br /> [31]OH S,MIN S L,ZHANG B T.Ensemble learning with active example selection for imbalanced biomedical data classification[J].IEEE/ACM Trans on Computational Biology and Bioinforma-tics,2011,8(2):316-325.<br /> [32]ZHANG X X,YANG T B,SRINIVASAN P.Online asymmetric active learning with imbalanced data[C]//Proceedings of ACM SIGKDD International Conferenceon Knowledge Discovery and Data Mining.ACM,2016:2055-2064.<br /> [33]AKBANI R,KWEK S,JAPKOWICZ N.Applying support vector machines to imbalanced datasets[C]//Proceedings of the 15th European Conference on Machine Learning.Springer Berlin Heidelberg,2004:39-50.<br /> [34]GAO W,ZHOU Z H.On the doubt about margin explanation of boosting[J].Artificial Intelligence,2013,203:1-18. |
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