一种新的分类方法:使用决策树 (NPULUD) 的基于社区的正面未标记的学习.
Bita Ghasemkhani1, Kadriye Filiz Balbal2, Kokten Ulas Birant3,4
1Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey.
Entropy (Basel, Switzerland)
|May 24, 2024
概括
这项研究引入了一种新的基于社区的积极无标记学习 (PU学习) 方法,使用决策树. 我们的新方法提高了对具有有限标记数据的数据集的分类准确性.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 标准二进制分类需要正和负样本,这并不总是可行的.
- 某些应用程序缺乏足够的标记数据,需要替代的学习策略.
研究的目的:
- 引入一种新的积极和未标记 (PU) 学习方法,称为使用决策树 (NPULUD) 的基于社区的积极未标记的学习.
- 为了应对只有正样本可用的建筑分类模型的挑战.
主要方法:
- NPULUD使用最接近邻居的方法来实现PU战略.
- 使用决策树算法进行分类,利用度测量来评估数据不确定性.
主要成果:
- 在24个现实数据集中,NPULUD方法实现了87.24%的平均准确率.
- 与最先进的方法相比,这代表了7.74%的统计学显著改善.
- 拟议的方法表现优于传统的监督学习,平均准确率为83.99%.
结论:
- 在有限的标记数据下,NPULUD为分类任务提供了有效的解决方案.
- 基于社区的战略和决策树与的整合显著提高了PU学习表现.
- 该方法表现出强大的性能和比现有技术的统计优势.
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