在数据集中缺少值的循环混合归算技术
Kurban Kotan1, Serdar Kırışoğlu2
1Department of Electrical Electronics and Computer Engineering, Graduate School of Education, Duzce University, 81620, Düzce, Turkey. kurbankotan@duzce.edu.tr.
Scientific reports
|February 24, 2025
概括
准确的缺失数据归算对于可靠的机器学习模型至关重要,特别是在医疗保健领域. 本研究引入了一种新的算法,它结合了基于行和基于列的归算,以在数据预处理中获得更高的准确性.
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:
- 缺失的数据显著影响机器学习模型的准确性和预测性能.
- 错误的归算方法,如平均值或模式,可以引入错误并降低可靠性.
- 数据集中缺失数据的高率损害了分析和建模的完整性.
研究的目的:
- 为了解决数据集中缺少数据归算的关键问题.
- 开发和评估一种新的算法,以实现更有效的缺失值赋值.
- 通过增强数据预处理,提高机器学习模型的性能.
主要方法:
- 提出了一个新的归算算法,循环结合基于行和基于列的技术.
- 算法考虑了个别的缺失值 (基于列) 和整体数据结构 (基于行).
- 在多个数据集上测试了算法,以评估其与现有方法相比的有效性.
主要成果:
- 当与特定的归算技术相结合时,拟议的算法在某些数据集上实现了100%的准确性.
- 与传统的归算方法相比,证明了更高的性能.
- 展示了整合基于行和基于列的归算策略的有效性.
结论:
- 精确的缺失数据归算导致机器学习模型的性能显著提高.
- 人工智能驱动的归算优于随机或经典方法.
- 新的循环归算算法为缺失数据挑战提供了高度准确的解决方案.
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