Author:
Huang Zhaoke,Yang Chunhua,Chen Xiaofang,Huang Keke,Xie Yongfang
Funder
National Natural Science Foundation of China
111 Project
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Reference41 articles.
1. Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-level-smote: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In: Theeramunkon T, Kijsirikul B, Cercone N, Ho TB (eds) Advances in knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 475–482.
https://doi.org/10.1007/978-3-642-01307-2_43
2. Cao H, Li X-L, Woon DY-K, Ng S-K (2013) Integrated oversampling for imbalanced time series classification. IEEE Trans Knowl Data Eng 25(12):2809–2822
3. Chawla NV (2003) C4. 5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure. In: Proceedings of the ICML, vol 3
4. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) Smote synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
5. Chawla NV, Lazarevic A, Hall LO, Bowyer KW (2003) Smoteboost: improving prediction of the minority class in boosting. In: European conference on principles of data mining and knowledge discovery, pp 107–119. Springer
Cited by
15 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献