NDLI: End-user feature labeling: a locally-weighted regression approach

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End-user feature labeling: a locally-weighted regression approach

Content Provider	ACM Digital Library
Author	Burnett, Margaret Oberst, Ian McIntosh, Kevin Das, Shubhomoy Stumpf, Simone Moore, Travis Wong, Weng-Keen
Abstract	When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions - especially in early stages, when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was both more effective than others at leveraging end users' feature labels to improve the learning algorithm, and more robust to real users' noisy feature labels. These results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively.
Starting Page	115
Ending Page	124
Page Count	10
File Format	PDF
ISBN	9781450304191
DOI	10.1145/1943403.1943423
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-02-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Intelligent interfaces Machine learning Feature labeling Locally weighted logistic regression
Content Type	Text
Resource Type	Article

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