基于亚空间的随机集体分类器用于高维数据 使用SPARK
Venkaiah Chowdary Bhimineni1, Rajiv Senapati1
1Department of CSE, SRM University, AP, Amaravati, Mangalagiri, Andhra Pradesh, India.
PloS one
|March 11, 2026
概括
本研究引入了一种改进的基于子空间的集体分类器 (ISSBEC),以应对高维数据分类的挑战. 这种新的方法提高了处理稀疏,高维数据集的机器学习模型的准确性和稳定性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 高维数据分类面临诸如稀疏性和过度适配等挑战,原因是"维度的诅咒".
- 越来越多的特征导致数据稀疏,阻碍了分类泛化,增加了计算成本,降低了准确性.
- 现有的方法在具有众多特征的数据集上的可扩展性和性能方面扎.
研究的目的:
- 提出一种新的集体分类器,旨在克服高维数据分类的局限性.
- 提高机器学习模型在高维设置中的准确性,稳定性和可扩展性.
- 引入一个有效处理特征选择和融合的框架,以提高分类性能.
主要方法:
- 使用min-max规范化的数据规范化.
- 在主节点上通过改进的深度模糊集群 (IDFC) 进行数据分区.
- 使用支持矢量机器修改的递归特征消除 (SVM-MRFE) 和在奴隶节点上的特征融合进行特征选择.
- 一个改进的基于子空间的集体分类器 (ISSBEC),集成基于特征融合的随机子空间 (FF-RSS),混合空间增强 (MSE) 和多个基础分类器.
主要成果:
- 与最先进的方法相比,拟议的ISSBEC分类器表现出更好的准确性和稳定性.
- 实验结果验证了拟议方法在处理高维数据集方面的有效性.
- 该框架为涉及大型特征集的机器学习任务提供了可扩展的解决方案.
结论:
- ISSBEC分类器为高维数据分类挑战提供了有效的解决方案.
- 拟议的特征选择,融合和组合分类方法显著提高了模型性能.
- 基于Spark的实现确保了现实世界的应用程序的可扩展性和效率.
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