现代协同神经网络用于不平衡的小数据分类
Zihao Wang1, Haifeng Li2, Lin Ma1
1Faculty of Computing, Harbin Institute of Technology, No.92, Xidazhi Street, Nangang District, Harbin, 150001, Heilongjiang, China.
现代协同神经网络 (MSNN) 通过纠正状态初始化和自我学习注意力参数来改善不平衡数据的深度学习. 这提高了对机器学习任务的分类性能和适应性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 由于过度拟合,深度学习与不平衡的小数据集作斗争.
- 循环神经网络提供了稳定性,但协同神经网络 (SNN) 面临关联错误.
- 目前的SNN研究经常使用遗传算法,限制参数优化.
研究的目的:
- 为了引入现代协同神经网络 (MSNN) 模型.
- 解决关联错误并增强SNN应用程序功能.
- 提高对不平衡数据集的分类性能.
主要方法:
- 纠正状态初始化以解决关联错误.
- 使用错误反向传播和梯度绕过来优化注意力参数.
- 通过自我学习注意力参数,实现与其他网络层进行联合培训.
主要成果:
- 通过纠正状态初始化,MSN释放了参数优化空间.
- 自学注意力参数适应不平衡的样本大小,促进分类.
- 在75个UCI机器学习数据集分类任务中,MSNN获得了最佳平均排名.
结论:
- MSNN有效地克服了SNN的限制,特别是关联错误.
- 该模型在不平衡的数据上展示了卓越的适应性和分类性能.
- MSNN显著优于现有的神经和非神经机器学习方法.
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