线性指数式损失结合了对不平衡分类的深度学习
Saiji Fu1, Duo Su2, Shilin Li3
1School of Economics and Management, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
ISA transactions
|June 29, 2023
概括
本研究介绍了DLINEX,这是一种新的深度学习损失函数,旨在解决阶级不平衡问题. 在不平衡的数据集中,DLINEX有效地提高了少数群体类别的分类准确性.
科学领域:
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 数据科学数据科学数据科学
背景情况:
- 阶级不平衡是机器学习的一个持续挑战,经常导致少数阶级的表现不佳.
- 传统方法在不平衡的数据中扎,将少数样本错误地归类为多数样本,这对现实世界有重大影响.
研究的目的:
- 引入一个新的深度学习损失函数,DLINEX,以有效地处理类失衡问题.
- 扩展线性指数 (LINEX) 损失函数用于多类不平衡的分类任务.
主要方法:
- 该研究提出了DLINEX,这是LINEX损失函数的多类扩展,适用于深度学习.
- DLINEX具有不对称的几何形状,允许通过单个参数对少数和难以分类的样本进行适应性聚焦.
- 它同时通过考虑实例属性来优化类间和类内部的多样性.
主要成果:
- 在CIFAR-10 (不平衡比率200) 上,DLINEX获得了42.08%的G-平均值,在HAM10000上获得了79.06%的G-平均值,在DRIVE,CHASEDB1和STARE数据集上获得了高的F1得分.
- 实验证明了DLINEX在图像级和像素级不平衡分类任务中的卓越性能.
结论:
- 在深度学习中,DLINEX为阶级不平衡提供了有效的解决方案.
- 拟议的损失函数在识别不同数据集中的少数和具有挑战性的样本方面取得了显著的改进.
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