由于图像分类不平衡而导致多数集群
Keshav Sharma1, Jyoti Arora1, Pooja Kherwa2
1Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India.
PeerJ. Computer science
|September 24, 2025
概括
图像分类中的类不平衡是通过不平衡图像分类的多数集群 (MCIIC) 来解决的. 这种方法通过聚类多数类来平衡数据集,改善少数类预测和整体模型性能.
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:
- 阶级不平衡是图像分类中的一个常见问题,导致偏见的模型在少数阶级表现不佳.
- 这种不平衡会对图像分类系统的整体可靠性和性能产生负面影响.
- 现有的方法往往难以有效地解决阶级之间和阶级内部的不平衡问题.
研究的目的:
- 引入一种新的不足采样技术,即不平衡图像分类的多数集群 (MCIIC),以减轻图像数据集中的类不平衡.
- 将带有不平衡数据的二进制分类问题转化为多类问题,以获得更平衡的解决方案.
- 改进在数据集中的罕见样本的检测.
主要方法:
- 采用了不足样本的方法,重点是减少多数类样本.
- 无监督集群是用来将多数类划分为不同的集群.
- 使用肘法来确定多数类的最佳集群数量,每个集群被赋予一个新的标签.
主要成果:
- 该MCIIC技术有效地创建了一个更平衡的类分布,解决两者之间和类内的不平衡.
- 对基准数据集的实证评估表明,对不平衡的图像数据集的预测性能有显著的改进.
- 该研究显示,对模型准确性,精度,回忆和F1分数产生积极影响.
结论:
- MCIIC是一种实用且有效的预处理步骤,用于处理不平衡的图像数据集.
- 拟议的方法对不平衡的分类任务提供了相对于传统方法的显著改进.
- 这种技术提高了处理偏斜数据分布的机器学习模型的可靠性和性能.
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