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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14167
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| Title: | Variance-based Active Learning |
| Authors: | Saar-Tsechansky, Maytal
Provost, Foster |
| Issue Date: | 2000 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-00-05 |
| Abstract: | For many supervised learning tasks, the cost of acquiring training data
is dominated by the cost of class labeling. In this work, we explore
active learning for class probability estimation (CPE). Active learning
acquires data incrementally, using the model learned so far to help
identify especially useful additional data for labeling. We present a
new method for active learning, BootstrapLV, which chooses new data
based on the variance in probability estimates from bootstrap samples.
We then show empirically that the method reduces the number of data
items that must be labeled, across a wide variety of data sets. We also
compare Bootstrap-LV with Uncertainty Sampling, an existing
active-learning method for maximizing classification accuracy, and show
not only that BootstrapLV dominates for CPE but also that it is quite
competitive even for accuracy maximization. |
| URI: | http://hdl.handle.net/2451/14167 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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