Robust Classification for Imprecise Environments

Details Robust Classification for Imprecise Environments Robust Classification for Imprecise Environments

2000-09-13

arxiv.org/abs/cs/0009007v1

Computer Science

Learning

24 pages, 12 figures. To be published in Machine Learning Journal. For related papers, see http://www.hpl.hp.com/personal/Tom_

Scientific paper

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 (ROCCH) 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 analyses. Finally, we point to empirical evidence that a robust hybrid classifier indeed is needed for many real-world problems.

Affiliated with

Also associated with

No associations

Rating

Robust Classification for Imprecise Environments does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Robust Classification for Imprecise Environments, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust Classification for Imprecise Environments will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-566904