KA-Ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling-Reference-Cited by-同舟云学术

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KA-Ensemble: towards imbalanced image classification ensembling under-sampling and over-sampling

Published:2019-06-26 Issue:21-22 Volume:79 Page:14871-14888
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Ding Hao,Wei Bin,Gu Zhaorui,Yu Zhibin,Zheng Haiyong^ORCID,Zheng Bing,Li Juan

Funder

National Natural Science Foundation of China

Qingdao Municipal Science and Technology Program

China Scholarship Council

Foundation of Shandong provincial Key Laboratory of Digital Medicine and Computer Assisted Surgery

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/content/pdf/10.1007/s11042-019-07856-y.pdf

Reference50 articles.

1. Cenggoro TW et al (2018) Deep learning for imbalance data classification using class expert generative adversarial network. Procedia Comput Sci 135:60–67

2. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

3. 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. Springer, pp 107–119

4. Cherkassky V (1997) The nature of statistical learning theory. IEEE Trans Neural Netw 8(6):1564

5. Domingos P, Pazzani M (1997) On the optimality of the simple Bayesian classifier under zero-one loss. Mach Learn 29(2–3):103–130

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