End-User Feature Labeling via Locally Weighted Logistic Regression

Wong, W.-K., Oberst, I., Das, S., Moore, T., Stumpf, S. , McIntosh, K. and Burnett, M. (2011) End-User Feature Labeling via Locally Weighted Logistic Regression. In: Twenty-Fifth AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, 07-11 Aug 2011, pp. 1575-1578. ISBN 9781577355076

Full text not currently available from Enlighten.

Publisher's URL: https://ojs.aaai.org/index.php/AAAI/article/view/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.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Stumpf, Dr Simone
Authors: Wong, W.-K., Oberst, I., Das, S., Moore, T., Stumpf, S., McIntosh, K., and Burnett, M.
College/School:College of Science and Engineering > School of Computing Science
ISSN:2159-5399
ISBN:9781577355076
Published Online:04 August 2011

University Staff: Request a correction | Enlighten Editors: Update this record