计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 50-55.doi: 10.11896/j.issn.1002-137X.2019.02.008
金旭1, 王磊1, 孙国梓1,2,3, 李华康1,2,3
JIN Xu1, WANG Lei1, SUN Guo-zi1,2,3, LI Hua-kang1,2,3
摘要： 针对目前的分类算法在不均衡数据集上的分类效果不理想的问题,将监督学习和无监督学习相结合,提出了一种基于质心的欠采样——ICIKMDS。在现实应用中,一些数据并不容易获得,或者不同类型的数据本身在数量上就存在着差异性,因此造成了数据集分布的不均,如疾病检测中疾病患者和正常人比例的不均、信用卡欺诈中欺诈用户和正常用户比例的不均等。所提方法很好地解决了数据集不均衡的问题,首先通过求解样本之间的欧氏距离得到初始质心,然后采用k-means算法在大类样本集上进行聚类,使不均衡数据集在分布上更加均衡,有效地改善了分类器的分类效果。所提方法使分类器在测试集小类上的分类准确率远远高于随机欠采样和SMOTE算法,在整个测试集上的准确率几乎与其他算法相同。
中图分类号:
| [1]ZHAI Y,YANG B R,QU W.Overview of Imbalanced Data Mining [J].Computer Science,2010,37(10):27-32.(in Chinese) 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. [2]VISA S,RALESCU A.Issues in Mining Imbalanced Data Sets-A Review Paper[C]∥ Sixteen Midwest Artificial Intelligence & Cognitive Science Conference.2005:67-73. [3]XIE N N.Text classification algorithm based on unbalanced data set [D].Chongqing:Chongqing University,2013.(in Chinese) 谢娜娜.基于不均衡数据集的文本分类算法研究[D].重庆:重庆大学,2013. [4]YOU M Y,CHEN Y,LI G Z.New feature selection algorithm in unbalanced problem Im-IG [J].Journal of Shandong University (Engineering Science),2010,40(5):123-128.(in Chinese) 尤鸣宇,陈燕,李国正.不均衡问题中的特征选择新算法Im-IG[J].山东大学学报(工学版),2010,40(5):123-128. [5]HAN H,WANG W Y,MAO B H.Borderline-SMOTE:A New Over-Sampling Method in Imbalanced Data Sets Learning[M]∥Huang DS.,Zhang XP.,Huang GB.(eds) Advances in Intelligent Computing.Berlin:Springer,2005:878-887. [6]FAN X N.Data imbalance classification problem[D].Hefei:University of Science and Technology of China,2011.(in Chinese) 范先念.数据不平衡分类问题研究[D].合肥:中国科学技术大学,2011. [7]UÑEZ H,GONZALEZ-ABRIL L,ANGULO C.Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias[J].Journal of Classfication,2017,34(3):427-443. [8]ABDELLATIF S,BEN HASSINE M A,BEN YAHIA S,et al.ARCID:A New Approach to Deal with Imbalanced Datasets Classification[C]∥International Conference on Current Trends in Theory and Practice of Informatics.2018:569-580. [9]ZHANG Y,FU P P,ZHANG Y T.Large scale data classification based on hierarchical clustering and resampling[J].Journal of Computer Applications,2013,33(10):2801-2803.(in Chinese) 张永,浮盼盼,张玉婷.基于分层聚类及重采样的大规模数据分类[J].计算机应用,2013,33(10):2801-2803. [10]CHAWLA N,BOWYER K,HALL L,et al.SMOTE:Synthetic Minority Over-sampling Technique[J].Journal of Artificial Intelligence Research,2002,16(1):321-357. [11]LONGADGE R,DONGRE S.Class Imbalance Problem in Data Mining:Review[C]∥International Journal of Computer Science and Network,2013,2(1). [12]GUO Y W,LIU X X.Study on the Method of Information Gain Feature Selection in Chinese Text Classification [J].Computer Engineering and Applications.2012,48(27):119-122.(in Chinese) 郭亚维,刘晓霞.中文文本分类中信息增益特征选择方法的研究[J].计算机工程与应用.2012,48(27):119-122. [13]YIN L Z,GE Y,XIAO K L,et al.Feature selection for high-dimensional unbalanced data[J].Neurocomputing,2013,105(1):3-11. [14]BAI P,ZHANG X B,ZHANG B,et al.Support vector machine theory and engineering application examples [M].Xi’an:Xi’an University of Electronic Science and Technology Press,2008:28-30.(in Chinese) 白鹏,张喜斌,张斌,等.支持向量机理论及工程应用实例[M].西安:西安电子科技大学出版社,2008:28-30. [15]AKBANI R ,KWEK S,JAPKOWICZ N.Applying Support Vector Machines to Imbalanced Datasets[C]∥ European Conference on Machine Learning.Springer Berlin Heidelberg,2004. [16]DIAO C X.W-SVM Model for Unbalanced Data Set Classification [D].Hefei:Hefei University of Technology,2012.(in Chinese) 刁翠霞.面向不均衡数据集分类的W-SVM模型[D].合肥:合肥工业大学,2012. [17]GUO H Y,VIKTOR H L.Learning from Imbalanced Data Sets with Boosting and Data Generation:The DataBoost-IM Approach[J].Acm Sigkdd Explorations Newsletter,2004,6(1):30-39. |
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