Computer Science ›› 2018, Vol. 45 ›› Issue (7): 31-37.doi: 10.11896/j.issn.1002-137X.2018.07.005
• CCF Big Data 2017 • Previous Articles Next Articles
CHEN Sheng-ling ,SHEN Si-qi, LI Dong-sheng
CLC Number:
[1]HE H,GARCIA E A.Learning from Imbalanced Data[J].IEEE Transactions on Knowledge & Data Engineering,2009,21(9):1263-1284. [2]周志华.机器学习[M].北京:清华大学出版社,2016. [3]CHAWLA N V,BOWYER K W,HALL L O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research,2002,16(1):321-357. [4]CHAWLA N,LAZAREVIC A,HALL L,et al.SMOTEBoost:Improving prediction of the minority class in boosting[C]∥European Conference on Knowledge Discovery in Databased:PKDD.2003:107-119. [5]HE H,BAI Y,GARCIA E A,et al.ADASYN:Adaptive syn-thetic sampling approach for imbalanced learning[C]∥IEEE International Joint Conference on Neural Networks.IEEE,2008:1322-1328. [6]JIA A L,SHEN S,CHEN S,et al.An Analysis on a YouTube-like UGC site with Enhanced Social Features[C]∥Proceedings of the 26th International Conference on World Wide Web Companion.2017:1477-1483. [7]HAN H,WANG W Y,MAO B H.Borderline-SMOTE:a newover-sampling method in imbalanced data sets learning[C]∥International Conference on Intelligent Computing.Berlin,Springer,Heidelberg,2005:878-887. [8]CIESLAK D A,CHAWLA N V,STRIEGEL A.Combating imbalance in network intrusion datasets[C]∥IEEE International Conference on Granular Computing.IEEE,2006:732-737. [9]LI M,FAN S.CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests[J].Bmc Bioinformatics,2017,18(1):169. [10]LI J,FONG S,SUNG Y,et al.Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification[J].Biodata Mining,2016,9(1):37. [11]LIU X Y,WU J,ZHOU Z H.Exploratory Undersampling for Class-Imbalance Learning[J].IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society,2009,39(2):539-550. [12]SEIFFERT C,KHOSHGOFTAAR T M,VAN HULSE J,et al.RUSBoost:A hybrid approach to alleviating class imbalance[J].IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,2010,40(1):185-197. [13]RODRGUEZ J J,KUNCHEVA L I,ALONSO C J.Rotation forest:A new classifier ensemble method[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2006,28(10):1619-1630. [14]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal Loss for Dense Object Detection[OL].http://www.researchgate.net/publication/322059369-Focal-Loss-for-Dense_Object-Detection. [15]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative adversarial nets[C]∥International Conference on Neural Information Processing Systems.MIT Press,2014:2672-2680. [16]ARTHUR A,DAVID N.The UCI Machine Learning Repository.http://archive.ics.uci.edu/ml/datasets.html. [17]CHEN S,HE H,GARCIA E A.RAMOBoost:Ranked Minority Oversampling in Boosting[J].IEEE Transactions on Neural Networks,2010,21(10):1624-1642. |
[1] | 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. |
[2] | 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. |
[3] | ZHOU Zhi-hao, CHEN Lei, WU Xiang, QIU Dong-liang, LIANG Guang-sheng, ZENG Fan-qiao. SMOTE-SDSAE-SVM Based Vehicle CAN Bus Intrusion Detection Algorithm [J]. Computer Science, 2022, 49(6A): 562-570. |
[4] | WANG Yu-fei, CHEN Wen. Tri-training Algorithm Based on DECORATE Ensemble Learning and Credibility Assessment [J]. Computer Science, 2022, 49(6): 127-133. |
[5] | HAN Hong-qi, RAN Ya-xin, ZHANG Yun-liang, GUI Jie, GAO Xiong, YI Meng-lin. Study on Cross-media Information Retrieval Based on Common Subspace Classification Learning [J]. Computer Science, 2022, 49(5): 33-42. |
[6] | DONG Qi-da, WANG Zhe, WU Song-yang. Feature Fusion Framework Combining Attention Mechanism and Geometric Information [J]. Computer Science, 2022, 49(5): 129-134. |
[7] | REN Shou-peng, LI Jin, WANG Jing-ru, YUE Kun. Ensemble Regression Decision Trees-based lncRNA-disease Association Prediction [J]. Computer Science, 2022, 49(2): 265-271. |
[8] | CHEN Wei, LI Hang, LI Wei-hua. Ensemble Learning Method for Nucleosome Localization Prediction [J]. Computer Science, 2022, 49(2): 285-291. |
[9] | JIANG Hao-chen, WEI Zi-qi, LIU Lin, CHEN Jun. Imbalanced Data Classification:A Survey and Experiments in Medical Domain [J]. Computer Science, 2022, 49(1): 80-88. |
[10] | LIU Zhen-yu, SONG Xiao-ying. Multivariate Regression Forest for Categorical Attribute Data [J]. Computer Science, 2022, 49(1): 108-114. |
[11] | CHEN Le, GAO Ling, REN Jie, DANG Xin, WANG Yi-hao, CAO Rui, ZHENG Jie, WANG Hai. Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality [J]. Computer Science, 2022, 49(1): 194-203. |
[12] | ZHOU Xin-min, HU Yi-gui, LIU Wen-jie, SUN Rong-jun. Research on Urban Function Recognition Based on Multi-modal and Multi-level Data Fusion Method [J]. Computer Science, 2021, 48(9): 50-58. |
[13] | CHEN Jing-jie, WANG Kun. Interval Prediction Method for Imbalanced Fuel Consumption Data [J]. Computer Science, 2021, 48(7): 178-183. |
[14] | ZHOU Gang, GUO Fu-liang. Research on Ensemble Learning Method Based on Feature Selection for High-dimensional Data [J]. Computer Science, 2021, 48(6A): 250-254. |
[15] | DAI Zong-ming, HU Kai, XIE Jie, GUO Ya. Ensemble Learning Algorithm Based on Intuitionistic Fuzzy Sets [J]. Computer Science, 2021, 48(6A): 270-274. |
|