迈向快速交互机器学习:评估无表示分类的权衡

Dustin L. Arendt, Emily Saldanha, Ryan Wesslen, Svitlana Volkova, Wenwen Dou
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引用次数: 11

摘要

我们的贡献是设计和评估一个交互式机器学习界面,在每次交互后快速为用户提供模型反馈。为了解决可视的可伸缩性,该接口通过“冰山一角”的方法与用户通信,用户与每个类的一小组推荐实例进行交互。为了解决计算可扩展性问题,我们开发了一种O(n)分类算法,该算法以增量方式合并用户反馈,而无需咨询数据的底层表示矩阵。我们的计算评估表明,该算法在少量标记数据下具有与几种现成的分类算法相似的精度。经验评估显示,与同等的主动学习设置相比,使用我们的设计的用户表现更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards rapid interactive machine learning: evaluating tradeoffs of classification without representation
Our contribution is the design and evaluation of an interactive machine learning interface that rapidly provides the user with model feedback after every interaction. To address visual scalability, this interface communicates with the user via a "tip of the iceberg" approach, where the user interacts with a small set of recommended instances for each class. To address computational scalability, we developed an O(n) classification algorithm that incorporates user feedback incrementally, and without consulting the data's underlying representation matrix. Our computational evaluation showed that this algorithm has similar accuracy to several off-the-shelf classification algorithms with small amounts of labeled data. Empirical evaluation revealed that users performed better using our design compared to an equivalent active learning setup.
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