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End-User Feature Labeling via Locally Weighted Logistic Regression

Wong, W-K., Oberst, I., Das, S. , Moore, T., Stumpf, S., McIntosh, K. & Burnett, M. (2011). End-User Feature Labeling via Locally Weighted Logistic Regression. In: Burgard, W. & Roth, D. (Eds.), Proceedings of the Twenty-Fifth AAAI conference on Artificial Intelligence. (pp. 1575-1578). USA: AAAI Press.


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.

Publication Type: Book Section
Additional Information: Copyright AAAI, 2011.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science > Human Computer Interaction Design
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