End-User Feature Labeling via Locally Weighted Logistic Regression

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Wong, W-K. ; Oberst, I. ; Das, S. ; Moore, T. ; Stumpf, S. ; McIntosh, K. ; Burnett, M. (2011)
  • Publisher: AAAI Press
  • Subject: QA75

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.
  • References (13)
    13 references, page 1 of 2

    Attenberg, J., Melville, P., and Provost, F. 2010. A unified approach to active dual supervision for labeling features and examples. In Proc. ECML, 40-55. Berlin, Heidelberg: SpringerVerlag.

    Cleveland, W., and Devlin, S. 1988. Locally-weighted regression: An approach to regression analysis by local fitting. J. American Statistical Assn 83(403): 596-610.

    Deng, K. 1998. Omega: On-line Memory-Based General Purpose System Classifier. PhD Dissertation. Carnegie Mellon University, Pittsburgh, PA.

    Druck, G., Mann, G., and McCallum, A. 2008. Learning from labeled features using generalized expectation criteria. In Proc. SIGIR, 595-602. New York, NY: ACM Press.

    Lang, K. 1995. Newsweeder: Learning to filter netnews. In Proc. ICML, 331-339. San Mateo, CA: Morgan Kaufmann.

    Lewis, D. 2004. Reuters-21578. Available at http://www. daviddlewis.com/resoursce/testcollections/reuters21578.

    Lewis, D. D., Yang, Y., Rose, T., Li, F. 2004. RCV1: A new benchmark collection for text categorization research. JMLR 5: 361-397. http://www.jmlr.org/papers/ volume5/lewis04a/lewis04a.pdf.

    Raghavan, H., Madani, O., and Jones, R. 2006. Active Learning with Feedback on Both Features and Instances. JMLR 7: 1655- 1686.

    Raghavan, H. and Allan, J. 2007. An interactive algorithm for asking and incorporating feature feedback into support vector machines, In Proc. SIGIR, 79-86. New York, NY: ACM.

    Settles, B. 2009. Active learning literature survey. Technical Report 1648, Department of Computer Science, University of Wisconsin-Madison, Madison, WI.

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