数据区域上的分层主动学习与标签比例
Zhipeng Luo1, Qiang Gao2, Yazhou He3
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China.
概括
这项研究引入了一个新的积极学习框架,使用人类注释区域来构建分类模型. 这种方法显著减少了对数据注释所需的人力资源,在现实应用中被证明是有效的.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 计算生物学 计算生物学
背景情况:
- 对分类模型的基于实例的注释是耗时和昂贵的.
- 现有的主动学习方法通常依赖于广泛的实例级标签.
- 在模型培训中,需要有效的方法来减少人类注释的努力.
研究的目的:
- 提出一个新的积极学习框架,从人类注释的区域构建分类模型.
- 通过开发一个层次化的积极学习 (HAL) 框架来应对有限的初始区域的挑战.
- 通过多层次 (森林) 方法增强框架,为更具信息和多样化的地区提供更多信息.
主要方法:
- 开发了一个层次化的主动学习 (HAL) 框架,将数据空间逐渐划分为子区域.
- 利用来自标签比例算法的学习来训练使用区域标签和类比例的模型.
- 实施了多层次 (森林) 解决方案,以建立多个较浅的区域层次结构.
- 评估了各种分类数据集的框架和癌症生存率分析中的真实世界用户研究.
主要成果:
- 基于区域的积极学习方法可以有效地学习高质量的分类器.
- HAL框架显著减少了构建分类模型所需的人类注释工作.
- 在众多分类数据集和结直肠癌存活率分析研究中证明了框架的有效性.
结论:
- 拟议的基于区域的积极学习框架为降低注释成本提供了一个非常有效的解决方案.
- 来自区域的积极学习为传统的基于实例的注释提供了可行的替代方案.
- 层次和多层次的方法提高了从有限的标记区域模型学习的效率和质量.
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