基于聚类的CPSGrader主动学习

Garvit Juniwal, Sakshi Jain, Alexandre Donzé, S. Seshia
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引用次数: 3

摘要

在这项工作中,我们提出并评估了CPSGrader背景下的主动学习算法,CPSGrader是一种用于网络物理系统领域实验室课程的自动评分和反馈生成工具。CPSGrader使用测试台来检测某些类型错误的存在,这些测试台部分是通过机器学习从有错误和没有错误的解决方案中生成的(正面和负面示例)。我们开发了一种基于聚类的主动学习技术,该技术从大量未标记的解决方案数据库中选择少量的参考解决方案供专家标记,这些解决方案将用作训练数据。与随机选择相比,目标是在较少的参考解的情况下实现更高的故障识别精度。我们使用从加州大学伯克利分校的一门校内实验室课程中获得的数据来证明我们算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clustering-Based Active Learning for CPSGrader
In this work, we propose and evaluate an active learning algorithm in context of CPSGrader, an automatic grading and feedback generation tool for laboratory-based courses in the area of cyber-physical systems. CPSGrader detects the presence of certain classes of mistakes using test benches that are generated in part via machine learning from solutions that have the fault and those that do not (positive and negative examples). We develop a clustering-based active learning technique that selects from a large database of unlabeled solutions, a small number of reference solutions for the expert to label that will be used as training data. The goal is to achieve better accuracy of fault identification with fewer reference solutions as compared to random selection. We demonstrate the effectiveness of our algorithm using data obtained from an on-campus laboratory-based course at UC Berkeley.
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