层次化的积极学习与对地区的定性反
Zhipeng Luo1, Yazhou He2, Yanbing Xue3
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China.
概括
本研究介绍了基于区域的积极学习,这是一种用于机器学习的更快的人类监督方法. 它通过标记数据区域而不是单个实例来提高模型培训效率.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人与计算机的交互
背景情况:
- 培训分类模型通常需要广泛的标记数据.
- 基于实例的注释对于人类注释者来说通常是低效和耗时的.
研究的目的:
- 提出和评估一种使用数据区域的新型人类监督方法.
- 开发一种高效的模型学习方法,减少注释工作.
主要方法:
- 引入基于地区的标签,在数据子空间中评估定性类比例.
- 设计了一个层次化的积极学习过程,以递归构建一个区域层次结构.
- 集成的积极学习策略与人类专业知识,以提供歧视性特征.
主要成果:
- 在九个数据集上进行了广泛的实验,证明了该框架的有效性.
- 一项关于结直肠癌患者生存率分析的用户研究验证了这一方法.
- 基于区域的积极学习框架的表现优于传统的基于实例的方法.
结论:
- 基于区域的积极学习为基于实例的方法提供了更有效和更有效的替代方案.
- 这种方法显著减少了人类注释的努力,同时保持了模型的性能.
- 拟议的框架显示了实际机器学习应用的前景.
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