Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
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严格偏序的主动学习：概念先决关系案例研究
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
C. Lee Giles
中科院分区：
文献类型：
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作者：
Chen Liang;Jianbo Ye;H. Zhao;B. Pursel;C. Lee Giles
严格偏序是关系数据中常见的一种数学结构。大规模提取此类关系的一个障碍是缺乏用于构建有效的数据驱动解决方案的大规模标签。我们开发了一个主动学习框架来挖掘这些关系，并遵循严格的顺序。我们的方法结合了关系推理，不仅可以从现有的标签集中找到新的未标记对，而且还可以设计考虑标签关系结构的新查询策略。我们对概念前提关系的实验表明，与其他基线方法相比，我们提出的框架可以在相同的查询预算下大幅提高分类性能。
Strict partial order is a mathematical structure commonly seen in relational data. One obstacle to extracting such type of relations at scale is the lack of large-scale labels for building effective data-driven solutions. We develop an active learning framework for mining such relations subject to a strict order. Our approach incorporates relational reasoning not only in finding new unlabeled pairs whose labels can be deduced from an existing label set, but also in devising new query strategies that consider the relational structure of labels. Our experiments on concept prerequisite relations show our proposed framework can substantially improve the classification performance with the same query budget compared to other baseline approaches.