End-User Feature Labeling via Locally Weighted Logistic Regression


  • 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


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.




How to Cite

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. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1575-1578. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7961



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