Journal of Shanghai Jiaotong University >
Unbalanced Learning of Generative Adversarial Network Based on Latent Posterior
Received date: 2019-09-16
Online published: 2021-06-01
Based on the problem that the oversampling method in the existing unbalanced classification problem cannot fully utilize the data probability density distribution, a method named latent posterior based generative adversarial network for oversampling (LGOS) was proposed. This method used variational auto-encoder to obtain the approximate posterior distribution of latent variable and generation network could effectively estimate the true probability distribution function of the data. The sampling in the latent space could overcome the randomness of generative adversarial network. The marginal distribution adaptive loss and the conditional distribution adaptive loss were introduced to improve the quality of generated data. Besides, the generated samples as source domain samples were put into the transfer learning framework, the classification algorithm of transfer learning for boosting with weight scaling (TrWSBoost) was proposed, and the weight scaling factor was introduced, which effectively solved the problem that the weight of source domain samples converge too fast and lead to insufficient learning. The experimental results show that the proposed method is superior to the existing oversampling method in the performance of common metrics.
HE Xinlin, QI Zongfeng, LI Jianxun . Unbalanced Learning of Generative Adversarial Network Based on Latent Posterior[J]. Journal of Shanghai Jiaotong University, 2021 , 55(5) : 557 -565 . DOI: 10.16183/j.cnki.jsjtu.2019.264
| [1] | FOTOUHI S, ASADI S, KATTAN M W. A comprehensive data level analysis for cancer diagnosis on imbalanced data[J]. Journal of Biomedical Informa-tics, 2019, 90:103089. |
| [2] | NAMVAR A, SIAMI M, RABHI F, et al. Credit risk prediction in an imbalanced social lending environment[J]. International Journal of Computational Intelligence Systems, 2018, 11(1):925-935. |
| [3] | SOLEYMANI R, GRANGER E, FUMERA G. Progressive boosting for class imbalance and its application to face re-identification[J]. Expert Systems With Applications, 2018, 101:271-291. |
| [4] | LEE T, LEE K B, KIM C O. Performance of machine learning algorithms for class-imbalanced process fault detection problems[J]. IEEE Transactions on Semiconductor Manufacturing, 2016, 29(4):436-445. |
| [5] | CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16:321-357. |
| [6] | HAN H, WANG W Y, MAO B H. Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning [C]//International Conference on Intelligent Computing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005: 878-887. |
| [7] | HE H B, BAI Y, GARCIA E A, et al. ADASYN: Adaptive synthetic sampling approach for imbalanced learning [C]//2008 IEEE International Joint Conference on Neural Networks. Piscataway, NJ, USA: IEEE, 2008: 1322-1328. |
| [8] | BARUA S, ISLAM M M, YAO X, et al. MWMOTE: Majority weighted minority oversampling technique for imbalanced data set learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2):405-425. |
| [9] | DOUZAS G, BACAO F. Effective data generation for imbalanced learning using conditional generative adversarial networks[J]. Expert Systems With Applications, 2018, 91:464-471. |
| [10] | HE H B, GARCIA E A. Learning from imbalanced data[J]. IEEE Transactions on Knowledge and Data Engineering, 2009, 21(9):1263-1284. |
| [11] | SUN Y M, KAMEL M S, WONG A K C, et al. Cost-sensitive boosting for classification of imba-lanced data[J]. Pattern Recognition, 2007, 40(12):3358-3378. |
| [12] | CHAWLA N V, LAZAREVIC A, HALL L O, et al. SMOTEBoost: Improving prediction of the minority class in boosting [C]//European Conference on Principles of Data Mining and Knowledge Discovery. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003: 107-119. |
| [13] | CHEN S, HE H B, GARCIA E A. RAMOBoost: Ranked minority oversampling in boosting[J]. IEEE Transactions on Neural Networks, 2010, 21(10):1624-1642. |
| [14] | ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks [C]//2017 IEEE International Conference on Computer Vision. Piscataway, NJ, USA: IEEE, 2017: 2242-2251. |
| [15] | ZHANG H, XU T, LI H S, et al. StackGAN: Realistic image synjournal with stacked generative adversarial networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(8):1947-1962. |
| [16] | GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems-Volume 2. Cambridge, MA, USA: MIT Press, 2014: 2672-2680. |
| [17] | PAN S J, TSANG I W, KWOK J T, et al. Domain adaptation via transfer component analysis[J]. IEEE Transactions on Neural Networks, 2011, 22(2):199-210. |
| [18] | LONG M S, WANG J M, DING G G, et al. Transfer feature learning with joint distribution adaptation [C]//2013 IEEE International Conference on Computer Vision. Piscataway, NJ, USA: IEEE, 2013: 2200-2207. |
| [19] | LONG M S, ZHU H, WANG J M, et al. Deep transfer learning with joint adaptation networks [C]//ICML'17: Proceedings of the 34th International Conference on Machine Learning-Volume 70. New York, NY, USA: ACM, 2017: 2208-2217. |
| [20] | DAI W Y, YANG Q, XUE G R, et al. Boosting for transfer learning[C]//Proceedings of the 24th International Conference on Machine Learning-ICML '07. New York: ACM Press, 2007: 193-200. |
| [21] | 王胜涛. 基于迁移过采样的类别不平衡学习算法研究[D]. 南京: 东南大学, 2017. |
| [21] | WANG Shengtao. Research on transfer-sampling based method for class-imbalance learning[D]. Nanjing: Southeast University, 2017. |
| [22] | 么素素, 王宝亮, 侯永宏. 绝对不平衡样本分类的集成迁移学习算法[J]. 计算机科学与探索, 2018, 12(7):1145-1153. |
| [22] | YAO Susu, WANG Baoliang, HOU Yonghong. Ensemble transfer learning algorithm for absolute imbalanced data classification[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(7):1145-1153. |
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