Budgeted
Learning
Some machine learning applications have training data where
the labels are available but the attributes describing the
examples must be purchased. This problem is, in a sense, a
dual to active learning. Budgeted learning algorithms
attempt to intelligently choose the attributes to purchase
in order to learn well with as few purchased attributes as
possible.
Kun Deng, Chris Bourke, Stephen Scott, Julie Sunderman, and
Yaling Zheng. Bandit-Based Algorithms for Budgeted
Learning. In Proceedings of the Seventh IEEE International
Conference on Data Mining. October 2007, pages 463-468.
Abstract
paper (849Kb PDF)
Active
Learning
Some machine learning applications have abundant amounts of
unlabeled data with an oracle that is capable of labeling a
relatively small number of these examples to be used in
supervised training. Active learning algorithms attempt to
intelligently choose the examples to label in order to
learn well with as few labeled examples as possible.
Matt Culver, Deng Kun, and Stephen Scott. Active Learning
to Maximize Area Under the ROC Curve. In Proceedings of the
Sixth IEEE International Conference on Data Mining.
December 2006, pages 149-158.
Abstract
paper (146Kb PDF)
Thomas Osugi, Deng Kun, and Stephen Scott. Balancing
Exploration and Exploitation: A New Algorithm for Active
Machine Learning. In Proceedings of the Fifth IEEE
International Conference on Data Mining, pages 330-337.
November 2005.
Abstract
paper (340Kb PDF)
Receiver Operating Characteristic (ROC)
Analysis
Receiver Operating Characteristic (ROC) analysis is an
alternative means of measuring classifier peroformance. We
study mechanisms of adjusting multi-class classifiers to
improve performance (with respect to its ROC hypersurface)
when faced with nonuniform misclassification costs.
Kun Deng, Chris Bourke, Stephen Scott, and N. V.
Vinodchandran. New Algorithms for Optimizing Multi-Class
Classifiers via ROC Surfaces. In Proceedings of The Third
Workshop on ROC Analysis in Machine Learning, pages 17-24,
June 2006.
Abstract
264Kb PDF
Chris Bourke, Kun Deng, Stephen D. Scott, Robert Schapire,
and N. V. Vinodchandran. On reoptimizing multi-class
classifiers. Machine Learning, 71(2–3):219–242;
doi:10.1007/s10994-008-5056-8, 2008.
Abstract
On-line version