适应性邻居粗略设置模型用于混合数据处理:帕金森病行为分析的案例研究
Imran Raza1, Muhammad Hasan Jamal1, Rizwan Qureshi1
1Department of Computer Science, COMSATS University Islamabad, Lahore Campus, Lahore, 54000, Pakistan.
Scientific reports
|April 1, 2024
概括
这项研究引入了一种新的混合数据处理模型,使用邻近粗略集来有效分析混合数据. 适应性方法提高了数据挖掘的准确性,在帕金森病分析中达到95%.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 混合数据,结合了分类和数字类型,提出了重要的知识提取挑战.
- 现有的混合数据处理模型需要进行彻底的比较,才能有效地进行数据挖掘.
- 邻近粗略集为处理混合数据中的不确定性提供了一个有希望的方法.
研究的目的:
- 分析和比较邻里粗略的基于集的混合数据处理模型.
- 为高效的混合数据处理提出一个通用邻里粗略的基于集的混合模型.
- 为了提高数据挖掘的有效性,而不是离散,保存信息和实际意义.
主要方法:
- 为混合数据开发了一个通用的邻里粗略的基于集的混合模型.
- 实现了邻近近似空间值的动态适应.
- 利用帕金森病患者的测试台来评估该方案.
主要成果:
- 拟议的方案有效地适应性地处理数值和分类数据.
- 在帕金森病数据集上实现了95%的准确性.
- 在适应性混合数据处理方面表现优于现有方案.
结论:
- 这项研究为混合数据处理挑战提供了强大而适应性的解决方案.
- 拟议的模型提高了数据挖掘的有效性,特别是对于复杂的数据集,如医学分析中的数据集.
- 这项工作推进了混合数据处理技术,提供了更好的准确性和信息保存.
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