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使用新型GBO和SSG增强和改进不平衡类数据的性能:比较分析

Md Manjurul Ahsan1, Md Shahin Ali2, Zahed Siddique3

  • 1School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK 73019, USA.

Neural networks : the official journal of the International Neural Network Society
|February 9, 2024
PubMed
概括

本研究介绍了基于生成对抗网络 (GAN) 的新型过量采样 (GBO) 和支持矢量机器-SMOTE-GAN (SSG) 技术,以解决机器学习中的类失衡问题 (CIP). 这些方法提高了少数群体类别的分类准确性,优于现有的方法.

关键词:
没有了,没有了,没有了.不平衡类数据不平衡类数据一个小样本小样本.神经网络的神经网络的神经网络过量采样过度采样在SMOTE中使用.

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