使用基于密度的边界识别来进行SVM培训的数据减少
Mohammed Shalaby1, Mohamed Farouk1, Hatem A Khater2
1Department of Computer Science, College of Computing and Information Technology, Arab Academy of Science, Technology & Maritime Transport, Alexandria, Egypt.
PloS one
|April 3, 2024
概括
本研究引入了基于密度的边界识别 (DBI),以减少支持矢量机 (SVM) 训练数据大小. 该方法有效地提取边境实例,加快培训和预测,同时保持分类准确性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 模式识别 模式识别
背景情况:
- 支持向量机 (SVM) 广泛用于分类和回归.
- 由于二次编程优化,SVM培训对于大型数据集而言在计算上是昂贵的.
- 现有的方法旨在通过选择关键实例,如支持向量来减少培训数据.
研究的目的:
- 介绍一种基于密度的新方法,即基于密度的边界识别 (DBI),用于减少SVM培训数据.
- 探索DBI的变化及其应用到使用缩小维度的更高维度数据.
- 评估DBI在最小化训练数据大小和提高计算效率方面的有效性.
主要方法:
- 开发了一种基于密度的方法 (DBI) 来从数据集中提取边界实例.
- 应用DBI到低维嵌入 (使用统一的多维近似和投影 - UMAP) 对于高维数据.
- 在诸如香,USPS和Adult9a等基准数据集上评估了多个DBI变异.
主要成果:
- 通过提取边境实例,DBI有效地减少了培训数据的大小.
- 该方法实现了显著的训练和预测加快.
- 与对完整数据集的培训相比,分类准确性得到了充分的维持.
- 印度央行表现出与BPLSH,CBCH和SE等最先进的方法相比具有竞争力.
结论:
- 拟议的基于密度的边界识别 (DBI) 方法有效地减少了SVM培训数据.
- 在大数据集的计算效率和加快速度方面,DBI提供了实际的好处.
- 该方法显示了在涉及大规模SVM培训的机器学习任务中实际应用的潜力.
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