Local Decision Pitfalls in Interactive Machine Learning: An Investigation into Feature Selection in Sentiment Analysis

ACM Transactions on Computer-Human Interaction (TOCHI), pp. 24-27, 2019.

Cited by: 6|Bibtex|Views142|DOI:https://doi.org/10.1145/3319616
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Other Links: dl.acm.org|dblp.uni-trier.de|academic.microsoft.com

Abstract:

Tools for Interactive Machine Learning (IML) enable end users to update models in a “rapid, focused, and incremental”—yet local—manner. In this work, we study the question of local decision making in an IML context around feature selection for a sentiment classification task. Specifically, we characterize the utility of interactive featur...More

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