[1] 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. [2] JAPKOWICZ N, STEPHEN S. The class imbalance problem:a systematic study[J]. Intelligent Data Analysis, 2002, 6(5):429-449. [3] CHEN X, GERLACH B, CASASENT D. Pruning support vectors for imbalanced data classification[C]//IJCNN 2005:Proceedings of the 2005 International Joint Conference on Neural Networks. Piscataway, NJ:IEEE, 2005, 3:1883-1888. [4] CHAN P K, STOLFO S J. Toward scalable learning with non-uniform class and cost distributions:a case study in credit card fraud detection[C]//KDD 1998:Proceedings of the 1998 ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Menlo Park, CA:AAAI Press, 1998:164-168. [5] LU B L, ITO M. Task decomposition and module combination based on class relations:a modular neural network for pattern classification[J]. IEEE Transactions on Neural Networks, 1999, 10(5):1244-1256. [6] LU B L, WANG K A, UTIYAMA M, et al. A part-versus-part method for massively parallel training of support vector machines[C]//IJCNN 2004:Proceedings of the 2004 International Joint Conference on Neural Networks. Piscataway, NJ:IEEE, 2004, 1:735:740. [7] CHAWLA N V, LAZAREVIC A, HALL L O, et al. SMOTEBoost:improving prediction of the minority class in boosting[C]//PKDD 2003:Proceedings of the 2003 European Conference on Principles of Data Mining and Knowledge Discovery. Berlin:Springer, 2003:107-119. [8] FU Z, WANG L, ZHANG D. An improved multi-label classification ensemble learning algorithm[C]//CCPR 2014:Proceedings of the 6th Chinese Conference on Pattern Recognition. Berlin:Springer, 2014:243-252. [9] 付忠良.多分类问题代价敏感AdaBoost算法[J].自动化学报,2011,37(8):973-983.(FU Z L. Cost-sensitive AdaBoost algorithm for multi-class classification problems[J]. Acta Automatica Sinica, 2011, 37(8):973-983.) [10] ZHOU Z H, LIU X Y. Training cost-sensitive neural networks with methods addressing the class imbalance problem[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1):63-77. [11] 王莉莉,付忠良.基于标签相关性的多标签分类AdaBoost算法[J].四川大学学报(工程科学版),2016,48(5):91-97.(WANG L L, FU Z L. Multi-label AdaBoost algorithm based on label correlations[J]. Journal of Sichuan University (Engineering Science Edition), 2016, 48(5):91-97.) [12] FAN W, STOLFO S J, ZHANG J, et al. AdaCost:misclassification cost-sensitive boosting[C]//ICML 1999:Proceedings of the 1999 International Conference on Machine Learning. San Francisco, CA:Morgan Kaufmann, 1999:97-105. [13] SCHAPIRE R E, SINGER Y. Improved boosting algorithms using confidence-rated predictions[J]. Machine Learning, 1999, 37(3):297-336. [14] WU T F, LIN C J, WENG R C. Probability estimates for multi-class classification by pairwise coupling[J]. Journal of Machine Learning Research, 2004, 5:975-1005. [15] ERTEKIN S, HUANG J, GILES C L. Active learning for class imbalance problem[C]//SIGIR 2007:Proceedings of the 2007 International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2007:823-824. [16] SUN Y, KAMEL M S, WANG Y. Boosting for learning multiple classes with imbalanced class distribution[C]//ICDM 2006:Proceedings of the 2006 International Conference on Data Mining. Washington, DC:IEEE Computer Society, 2006:592-602. [17] ZHANG M L, ZHOU Z H. ML-KNN:a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7):2038-2048. |