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Transfer synthetic over-sampling for class-imbalance learning with limited minority class data |
Xu-Ying LIU1,2,3(), Sheng-Tao WANG1,2,3, Min-Ling ZHANG1,2,3 |
1. School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China 3. Collaborative Innovation Center forWireless Communications Technology, Nanjing 210096, China |
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Abstract The problem of limited minority class data is encountered in many class imbalanced applications, but has received little attention. Synthetic over-sampling, as popular class-imbalance learning methods, could introduce much noise when minority class has limited data since the synthetic samples are not i.i.d. samples of minority class. Most sophisticated synthetic sampling methods tackle this problem by denoising or generating samples more consistent with ground-truth data distribution. But their assumptions about true noise or ground-truth data distribution may not hold. To adapt synthetic sampling to the problem of limited minority class data, the proposed Traso framework treats synthetic minority class samples as an additional data source, and exploits transfer learning to transfer knowledge from them to minority class. As an implementation, TrasoBoost method firstly generates synthetic samples to balance class sizes. Then in each boosting iteration, the weights of synthetic samples and original data decrease and increase respectively when being misclassified, and remain unchanged otherwise. The misclassified synthetic samples are potential noise, and thus have smaller influence in the following iterations. Besides, the weights of minority class instances have greater change than those of majority class instances to be more influential. And only original data are used to estimate error rate to be immune from noise. Finally, since the synthetic samples are highly related to minority class, all of the weak learners are aggregated for prediction. Experimental results show TrasoBoost outperforms many popular class-imbalance learning methods.
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Keywords
machine learning
data mining
class imbalance
over sampling
boosting
transfer learning
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Corresponding Author(s):
Xu-Ying LIU
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Just Accepted Date: 14 June 2018
Online First Date: 07 January 2019
Issue Date: 25 June 2019
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