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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27765
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| Title: | Robust Classification for Imprecise Environments |
| Authors: | Provost, Foster
Fawcett, Tom |
| Issue Date: | 1-Mar-2001 |
| Publisher: | Machine Learning |
| Citation: | Machine Learning 42, 203-231, 2001. |
| Series/Report no.: | CeDER-PP-2001-02 |
| Abstract: | In real-world environments it usually is difficult to specify target
operating conditions precisely, for example, target misclassification
costs. This uncertainty makes building robust classification systems
problematic. We show that it is possible to build a hybrid classifier
that will perform at least as well as the best available classifier for
any target conditions. In some cases, the performance of the hybrid
actually can surpass that of the best known classifier. This robust
performance extends across a wide variety of comparison frameworks,
including the optimization of metrics such as accuracy, expected cost,
lift, precision, recall, and workforce utilization. The hybrid also is
efficient to build, to store, and to update. The hybrid is based on a
method for the comparison of classifier performance that is robust to
imprecise class distributions and misclassification costs. The ROC
convex hull method combines techniques from ROC analysis, decision
analysis and computational geometry, and adapts them to the particulars
of analyzing learned classifiers. The method is efficient and
incremental, minimizes the management of classifier performance data,
and allows for clear visual comparisons and sensitivity analysis.
Finally, we point to empirical evidence that a robust hybrid classifier
indeed is needed for many real-world problems. |
| URI: | http://hdl.handle.net/2451/27765 |
| Appears in Collections: | CeDER Published Papers
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