积极学习与人类启发式:一个算法强大的标签偏见偏见
Sriram Ravichandran1, Nandan Sudarsanam2,3, Balaraman Ravindran2,3
1Department of Management Studies, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
Frontiers in artificial intelligence
|December 4, 2024
概括
积极学习中的人类标签偏见显著降低了模型性能. 一个新的反向信息密度算法,灵感来自于人类心理学,提高了87%的性能,证明了对偏见强大的积极学习策略的需求.
科学领域:
- 机器学习 机器学习
- 认知科学 认知科学
- 数据科学数据科学数据科学
背景情况:
- 积极学习通过自适应地选择数据进行标记来增强模型训练.
- 人类预言,像医生一样,可以引入由于认知启发式的标签偏见.
研究的目的:
- 调查人类启发式学习对主动学习表现的影响.
- 开发强大的积极学习算法来标记偏见.
主要方法:
- 评估了人类启发学的组合 (快速和节的树,计数模型) 与主动学习算法 (取样本,多视图学习,信息密度,反向信息密度) 和分类器.
- 在健康,营销和运输领域的15个现实数据集上进行了测试.
主要成果:
- 人类启发式标记显著降低了主动学习表现,有时低于随机机会.
- 建议的反向信息密度算法比其他方法提高了87%的性能.
结论:
- 模拟人类启发式学习对于设计有效的积极学习系统至关重要.
- 开发具有偏差强度的积极学习算法对于可靠的预测模型至关重要.
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