混合合成少数人过量采样技术 (HSMOTE) 和集体深度动态分类模型 (EDDCM) 用于大数据分析
Priyadharsini M1, Bhawana Tyagi2, Naga Priyadarsini R1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India.
Scientific reports
|November 11, 2025
概括
本研究引入了一种混合框架,通过解决阶级不平衡和高维度来改进大数据分类 (BDC). 这种新的方法提高了关键应用程序的机器学习模型性能.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 大数据分类 (BDC) 在医疗保健,电子商务和银行业非常重要.
- 传统的机器学习模型在BDC中与高维度和阶级不平衡作斗争.
- 现有的方法往往无法有效处理不平衡的数据集并选择相关特征.
研究的目的:
- 提出一个混合框架,提高大数据分类 (BDC) 的有效性.
- 解决BDC中高维度和阶级不平衡的挑战.
- 提高复杂数据集中的分类模型的可靠性和准确性.
主要方法:
- 引入混合合成少数人过量采样技术 (HSMOTE) 来处理类不平衡.
- 开发了使用元启发式算法 (FWDFA,AEHO,FWGWO) 的优化整体特征选择模型 (OEFSM),以进行强大的特征选择.
- 拟议的集体深度动态分类模型 (EDDCM) 集成DWCNN,DWBi-LSTM和WAE与动态集体策略.
主要成果:
- 拟议的框架在各种数据集中证明了更好的分类结果.
- 特别是在阶级不平衡和高维度的条件下,观察到显著的性能提升.
- 整合HSMOTE,OEFSM和EDDCM有效地提高了精度,回忆,F测量和准确性.
结论:
- 混合框架为大数据分类挑战提供了强大的解决方案.
- 先进的采样,特征选择和深度学习组合方法的结合提高了模型性能.
- 这种方法提供了一种可靠的方法来提高不平衡和高维数据的分类准确性.
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