Proj CMI Paper Reading: A Survey on Cost Types, Interaction Schemes, and Annotator Performance Models in Selection Algorithms for Active Learning in Classification

Abstract

背景：
Pool-based active learning (AL)目的在于优化注释过程
问题：人工注释者可能会有注释错误或者疲劳，可能不愿意响应
为此，提出的新主动学习AL策略有以下特点之一

1. 对人工注释进行建模，考虑专家不在场的情况
2. generalization of the interaction with human annotators through different query and annotation types, such as asking an annotator for feedback on an inferred classification rule 对人工注释交互进行泛化，引入不同的查询或者注释类型
3. consideration of complex cost schemes regarding annotations and misclassifications 为错误分类等注释问题引入其他复杂解决方案
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4. 引入了一个通用策略
5. 对大约60种方法进行了分类
6. 概述未来研究方向