使用基于DMN的中心点更新模型进行SVM-RFE启用功能选择,用于使用COVID-19进行增量数据集群
Robinson Joel M1, Manikandan G1, Bhuvaneswari G2
1Information Technology, Kings Engineering College, Sriperumbudur, India.
概括
本研究提出了一种新的增量数据聚类模型,使用纳米布甲虫五月算法 (NBMA). 该方法增强了特征选择和聚类准确性,以便更好地分析数据.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 增量数据集群对于分析不断变化的数据集至关重要.
- 现有的方法在特征选择和准确的重量更新方面面临挑战.
- 优化聚类算法需要高效的特征选择和适应性权重机制.
研究的目的:
- 引入一个有效的增量数据集群模型.
- 使用新的算法来增强功能选择和重量优化.
- 为了提高动态数据上的增量聚类的性能.
主要方法:
- 使用支持矢量机递归特征消除 (SVM-RFE) 进行特征选择.
- 体重参数优化和更新通过渐变纳米布甲虫五月算法 (NBMA).
- 使用权力k-平均值进行聚类,通过深度Maxout网络 (DMN) 进行中心点更新.
主要成果:
- 拟议的NBMA模型在增量数据集群中表现出卓越的性能.
- 优化的特征选择和权重更新带来了更好的集群精度.
- 集成DMN用于中心点更新增强了模型的适应性.
结论:
- 开发的入加权梯度NBMA模型为增量数据集群提供了有效的解决方案.
- 结合SVM-RFE,NBMA和DMN的混合方法提供了强大而准确的集群.
- 这项研究在处理动态和大规模数据集方面取得了重大进展.
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