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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.

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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.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.计入计算是指计入计算的方法.机器学习 机器学习缺失的值是指缺失的值.

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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.