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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 25 / No. 1: Twenty-Fifth AAAI Conference on Artificial Intelligence

End-User Feature Labeling via Locally Weighted Logistic Regression

March 8, 2023

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Authors

Weng-Keen Wong

Oregon State University


Ian Oberst

Oregon State University


Shubhomoy Das

Oregon State University


Travis Moore

Oregon State University


Simone Stumpf

City University London


Kevin McIntosh

Oregon State University


Margaret Burnett

Oregon State University


DOI:

10.1609/aaai.v25i1.7961


Abstract:

Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the learning algorithm by labeling more training data, this process is time consuming and too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on Locally Weighted Logistic 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 more effective than others at leveraging end users’ feature labels to improve the learning algorithm. Our 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.

Topics: AAAI

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Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett End-User Feature Labeling via Locally Weighted Logistic Regression Proceedings of the AAAI Conference on Artificial Intelligence, 25 (2011) 1575.

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett End-User Feature Labeling via Locally Weighted Logistic Regression AAAI 2011, 1575.

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett (2011). End-User Feature Labeling via Locally Weighted Logistic Regression. Proceedings of the AAAI Conference on Artificial Intelligence, 25, 1575.

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett. End-User Feature Labeling via Locally Weighted Logistic Regression. Proceedings of the AAAI Conference on Artificial Intelligence, 25 2011 p.1575.

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett. 2011. End-User Feature Labeling via Locally Weighted Logistic Regression. "Proceedings of the AAAI Conference on Artificial Intelligence, 25". 1575.

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett. (2011) "End-User Feature Labeling via Locally Weighted Logistic Regression", Proceedings of the AAAI Conference on Artificial Intelligence, 25, p.1575

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett, "End-User Feature Labeling via Locally Weighted Logistic Regression", AAAI, p.1575, 2011.

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett. "End-User Feature Labeling via Locally Weighted Logistic Regression". Proceedings of the AAAI Conference on Artificial Intelligence, 25, 2011, p.1575.

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett. "End-User Feature Labeling via Locally Weighted Logistic Regression". Proceedings of the AAAI Conference on Artificial Intelligence, 25, (2011): 1575.

Weng-Keen Wong|| Ian Oberst|| Shubhomoy Das|| Travis Moore|| Simone Stumpf|| Kevin McIntosh|| Margaret Burnett. End-User Feature Labeling via Locally Weighted Logistic Regression. AAAI[Internet]. 2011[cited 2023]; 1575.


ISSN: 2374-3468


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