Computer Science ›› 2019, Vol. 46 ›› Issue (11A): 194-198.
• Data Science • Previous Articles Next Articles
LIU Shu-dong, WEI Jia-min
CLC Number:
[1]PROBOST F.Machine learning from imbalanced data set 101[C]∥Proceedings of Workshop on Learning from Imbalanced Data Set (AAAI’00).Palo Alto,CA:AAAI,2000:1-3. [2]CHAWLA N V,JAPKOWICZ N,KOLCZ A.Editorial:specialissue on learning from imbalanced data sets[J].SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets,2004,6(1):1-6. [3]GALAR M,FERNANDEZ A,BARRENCHEA E,et al.A review on ensembles for the class imbalance problem:Bagging-,Boosting-,and hybrid-based approaches[J].IEEE Transaction on Systems,Man and Cybernetics,2012,42(4):463-484. [4]KRAWCZYK B.Learning from imbalanced data:open challenge and future directions[J].Progress in Artificial Intelligence,2016,5(4):1-12. [5]ROY A,CRUZ R M O,CAVALCANI G D C.A study on combining dynamic selection and data preprocessing for imbalanced learning[J].Neurocom-puting,2018,286:179-192. [6]GUO H,LI Y,JENNIFER S,et al.Learning from class-imba-lanced data:review of methods and applications[J].Expert Systems with Applications,2017,73:220-239. [7]YANG Q,WU X.10 challenging problems in data mining research[J].International Journal of Information Technology and Decision Making,2006,5(4):597-604. [8]FERNANDEZ A,RIO S,CHAWLA N V,et al.An insight into imbalanced big data classification:outcomes and challenges[J].Complex Intelligent Systems,2017,3(2):105-120. [9]GUERMAZI R,CHAABANE I,HAMMAMI M.AECID:asymmetric entropy for classifying imbalanced data[J].Information Sciences,2018,467:373-397. [10]WU F,JING X,SHIN S,et al.Multiset feature learning for highly imbalanced data classification[C]∥Proceedings of the thirty-first AAAI Conference on Artificial Intelligence.Palo Alto,CA:AAAI,2017:1583-1589. [11]LOYOLA-GONZALEZ O,MARTINEZ-TRINIDAD J F,CARRASCO-OCHOA J A.Study of the impact of resampling methods for contrast pattern based classifiers in imbalanced databases[J].Neurocomputing,2016,175:935-947. [12]LIN C,HSIEH T,LIN Y,et al.Minority Oversampling in Kernel Adaptive Subspaces for Class Imbalanced Datasets[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(5):950-962. [13]SHAHEE S A,ANANTHAKUMAR U.An adaptive oversampling technique for imbalanced datasets[C]∥Proceedings of IEEE International Conference on Data Mining (ICDM’18).NJ:IEEE,2018:1-16. [14]LIN W,TSAI C,HU Y,et al.Clustering-based undersampling in class-imbalanced data[J].Information Sciences,2017,409/410:17-26. [15]LI F,ZHANG X,ZHANG X,et al.Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets[J].Information Sciences,2018,422:242-256. [16]DECHERCHI S,ROCCHIA W.Import vector domain descrip-tion:a kernel logistic one-class learning algorithm[J].IEEE Transactions on Neural Networks and Learning Systems,2017,28(7):1722-1729. [17]FERNANDEZ-FRANCOS D,FONTENLA-ROMERO O,ALONSO-BETANZOS A.One-class convex hull-based algorithm for classification in distributed environments [J].IEEE Transactions on Systems,Man and Cybernetics,2017,99:1-11. [18]SUN J,SHAO J,HE C.Abnormal event detection for video surveillance using deep one-class learning[J].Multimedia Tools and Applications,2017,3:1-15. [19]ERFANI S M,REJASEGARAR S,KARUNA-SEKERA S,et al.High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J].Pattern Recognition,2016,58(C):121-134. [20]FERDOWSI Z,GHANI R,SETTIMI R.Online active learning with imbalanced Classes[C]∥Proceedings of IEEE 13th International Conference on Data Mining (ICDM’13),NJ:IEEE,2013:1043-1048. [21]ZHANG X,YANG T,SRINIVASAN P.Online asymmetric active learning with imbalanced data[C]∥Proceedings of 22th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’16).New York:ACM.2016:2055-2064. [22]RAMIREZ-LOAIZA M,SHARMA M,KUMAR G,et al.Active learning:An empirical study of common baselines[J].Data Mi-ning and Knowledge Discovery,2017,31:287-313. [23]ZHANG Y,ZHAO P,CAO J,et al.Online adaptive asymmetric active learning for budgeted imbalanced data[C]∥Proceedings of 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18).New York:ACM.2018:2768-2777. [24]LI K,KONG X,LU Z.Boosting weighted ELM for imbalanced learning[J].Neurocomputing,2014,128:15-21. [25]YU H,SUN C,YANG X,et al.ODC-ELM:optimal decisionoutputs compensation-based extreme learning machine for classifying imbalanced data[J].Knowledge-Based Systems,2016,92:55-70. [26]DING S,MIRZA B,LIN Z,et al.Kernel based online learning for imbalance multi- class classification[J].Neurocomputing,2018,277:139-148. [27]DUMPALA S H,CHAKRABORTY R,KOPPARAPU SK.A novel data representation for effective learning in class imbalanced scenarios[C]∥Proceedings of the Twenty-seventh International Joint Conference on Artificial Intelligence.2018:2100-2106. [28]贾洪杰,丁世飞,史忠植.求解大规模谱聚类的近似加权核k-means算法[J].软件学报,2015,26(11):2836-2846. [29]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. [30]HART P.The condensed nearest neighbor rule [J].IEEETransactions on Information Theory,1968,14:515-516. [31]TANG Y,ZHANG Y,CHAWLA N V,et al.SVMs modeling for highly imbalanced classification [J].IEEE Transactions on Systems,Man,and Cybernetics,2009,39(1):281-288. [32]GALAR M,FERNANDEZ A,BARRENECHEA E,et al.Eusboost:Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling [J].Pattern Recognition,2013,(12):3460-3471. [33]SEIFFERT C,KHOSHGOFTAAR T M,HULSE J V,et al.RUSBoost:a hybrid approach to alleviating class imbalance [J].IEEE Transactions on Systems,Man,and Cybernetics,2010,40(1):185-197. |
[1] | CHEN Zhi-qiang, HAN Meng, LI Mu-hang, WU Hong-xin, ZHANG Xi-long. Survey of Concept Drift Handling Methods in Data Streams [J]. Computer Science, 2022, 49(9): 14-32. |
[2] | ZHOU Xu, QIAN Sheng-sheng, LI Zhang-ming, FANG Quan, XU Chang-sheng. Dual Variational Multi-modal Attention Network for Incomplete Social Event Classification [J]. Computer Science, 2022, 49(9): 132-138. |
[3] | HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329. |
[4] | TAN Ying-ying, WANG Jun-li, ZHANG Chao-bo. Review of Text Classification Methods Based on Graph Convolutional Network [J]. Computer Science, 2022, 49(8): 205-216. |
[5] | YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236. |
[6] | WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25. |
[7] | GAO Zhen-zhuo, WANG Zhi-hai, LIU Hai-yang. Random Shapelet Forest Algorithm Embedded with Canonical Time Series Features [J]. Computer Science, 2022, 49(7): 40-49. |
[8] | YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang. Application of Graph Neural Network Based on Data Augmentation and Model Ensemble in Depression Recognition [J]. Computer Science, 2022, 49(7): 57-63. |
[9] | ZHANG Hong-bo, DONG Li-jia, PAN Yu-biao, HSIAO Tsung-chih, ZHANG Hui-zhen, DU Ji-xiang. Survey on Action Quality Assessment Methods in Video Understanding [J]. Computer Science, 2022, 49(7): 79-88. |
[10] | DU Li-jun, TANG Xi-lu, ZHOU Jiao, CHEN Yu-lan, CHENG Jian. Alzheimer's Disease Classification Method Based on Attention Mechanism and Multi-task Learning [J]. Computer Science, 2022, 49(6A): 60-65. |
[11] | LI Xiao-wei, SHU Hui, GUANG Yan, ZHAI Yi, YANG Zi-ji. Survey of the Application of Natural Language Processing for Resume Analysis [J]. Computer Science, 2022, 49(6A): 66-73. |
[12] | DENG Kai, YANG Pin, LI Yi-zhou, YANG Xing, ZENG Fan-rui, ZHANG Zhen-yu. Fast and Transmissible Domain Knowledge Graph Construction Method [J]. Computer Science, 2022, 49(6A): 100-108. |
[13] | HUANG Shao-bin, SUN Xue-wei, LI Rong-sheng. Relation Classification Method Based on Cross-sentence Contextual Information for Neural Network [J]. Computer Science, 2022, 49(6A): 119-124. |
[14] | LIN Xi, CHEN Zi-zhuo, WANG Zhong-qing. Aspect-level Sentiment Classification Based on Imbalanced Data and Ensemble Learning [J]. Computer Science, 2022, 49(6A): 144-149. |
[15] | KANG Yan, WU Zhi-wei, KOU Yong-qi, ZHANG Lan, XIE Si-yu, LI Hao. Deep Integrated Learning Software Requirement Classification Fusing Bert and Graph Convolution [J]. Computer Science, 2022, 49(6A): 150-158. |
|