Zadrozny B, Langford J, Abe N. Cost-sensitive learning by cost-proportionate example weighting. In: Proceedings of the 3rd IEEE International Conference on Data Mining. Washington D. C., USA: IEEE, 2003. 435-442
[2]
Ling C X, Sheng V S, Yang Q. Test strategies for cost-sensitive decision trees. IEEE Transactions of Knowledge and Data Engineering, 2006, 18(8): 1055-1067
[3]
Chai X Y, Deng L, Yang Q, Ling C X. Test-cost sensitive Naive Bayes classification. In: Proceedings of the 4th IEEE International Conference on Data Mining. Washington, D. C., USA: IEEE, 2004. 51-58
[4]
Ling C X, Sheng V S. A comparative study of cost-sensitive classifiers. Chinese Journal of Computers, 2007, 30(8): 1203-1211
[5]
Ting K M, Zheng Z. Boosting cost-sensitive trees. In: Proceedings of the 1st International Conference on Discovery Science. London, UK: Springer, 1999. 244-255
[6]
Fan W, Stolfo S J, Zhang J, Chan P K. AdaCost: misclassification cost-sensitive boosting. In: Proceedings of the 16th International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann, 1999. 97-105
Fu Zhong-Liang. An ensemble learning algorithm for direction prediction. Shanghai Jiaotong University (Science Edition), 2012, 46(2): 250-258 (付忠良. 一种用于方向预测的集成学习算法. 上海交通大学学报(自然版), 2012, 46(2): 250-258)
[9]
Fu Zhong-Liang. A universal ensemble learning algorithm. Journal of Computer Research and Development, 2013, 50(4): 861-872 (付忠良. 通用集成学习算法的构造. 计算机研究与发展, 2013, 50(4): 861-872)
[10]
Tsoumakas G, Katakis I. Multi-label classification: an overview. International Journal of Data Warehousing and Mining, 2007, 3(3): 1-13
[11]
Zhou Z H, Zhang M L, Huang S J, Li Y F. Multi-instance multi-label learning. Artificial Intelligence, 2012, 176(1): 2291-2320
[12]
Zhang M L, Zhou Z H. M3MIML: a maximum margin method for multi-instance multi-label learning. In: Proceedings of the 8th IEEE International Conference on Data Mining. Pisa, Italy: IEEE, 2008. 688-697
[13]
Trohidis K, Tsoumakas G, Kalliris G, Vlahavas I. Multi-label classification of music into emotions. In: Proceedings the 9th International Conference on Music Information Retrieval. Philadelphia, USA: Springer, 2008. 325-330
[14]
Boutell M R, Luo J B, Shen X P, Brown C M. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757-1771
[15]
Zhang M L, Zhou Z H. ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognition, 2007, 40(7): 2038-2048
[16]
Elisseeff A, Weston J. A kernel method for multi-labeled classification. In: Proceedings of Advances in Neural Information. Cambridge: MIT, 2001, 681-687
[17]
Yin Hui, Xu Jian-Hua, Xu Hua. A multi-label classification algorithm based on LS-SVM. Journal of Nanjing Normal University (Engineer and Technology Edition), 2010, 10(2): 68-73 (殷会, 许建华, 许花. 基于LS-SVM的多标签分类算法. 南京师范大学学报(工程技术版), 2010, 10(2): 68-73)
[18]
Benhouzid D, Busa-Fekete R, Cadagrande N, Collin F D, Kégl B. MultiBoost: a multi-purpose boosting package. Journal of Machine Learning Research, 2012, 13: 549-553
[19]
Cao Ying, Miao Qi-Guang, Liu Jia-Chen, Gao Lin. Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 2013, 39(6): 745-758 (曹莹, 苗启广, 刘家辰, 高琳. AdaBoost算法研究进展与展望. 自动化学报, 2013, 39(6): 745-758)
[20]
Schapire R E, Singer Y. Improved boosting algorithms using confidence-rated predictions. Machine Learning, 1999, 37(3): 297-336
[21]
Newman D J, Blake C, Merz C J. UCI repository of machine learning data bases [Online], available: http://archive. ics.uci.edu/ml/datasets.html, January 10, 2010.
[22]
Lo H Y, Wang J C, Wang H M, Lin H D. Cost-sensitive multi-label learning for audio tag annotation and retrieval. IEEE Transactions on Multimedia, 2011, 13(3): 518-529
[23]
Turney P. Types of cost in inductive concept learning. In: Proceedings of the Cost-Sensitive Learning Workshop at the 17th International Conference on Machine Learning. Stanford, USA: NRC, 2000. 15-21
[24]
Ting K M. An instance-weighting method to induce cost-sensitive trees. IEEE Transactions on Knowledge and Data Engineering, 2002, 14(3): 659-665
[25]
Domingos P. MetaCost: a general method for making classifiers cost-sensitive. In: Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 1999. 155-164
[26]
Elkan C. The foundations of cost-sensitive learning. In: Proceedings of the 17th International Joint Conference of Artificial Intelligence. San Francisco, USA: Morgan Kaufmann, 2001. 973-978
[27]
Bruka I, Kocková；S. A support for decision-making: cost-sensitive learning system. Artificial Intelligence in Medicine, 1999, 6(7): 67-82
