3,443 research outputs found
Multivariate Analysis from a Statistical Point of View
Multivariate Analysis is an increasingly common tool in experimental high
energy physics; however, many of the common approaches were borrowed from other
fields. We clarify what the goal of a multivariate algorithm should be for the
search for a new particle and compare different approaches. We also translate
the Neyman-Pearson theory into the language of statistical learning theory.Comment: Talk from PhyStat2003, Stanford, Ca, USA, September 2003, 4 pages,
LaTeX, 1 eps figures. PSN WEJT00
Robust Classification for Imprecise Environments
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.Comment: 24 pages, 12 figures. To be published in Machine Learning Journal.
For related papers, see http://www.hpl.hp.com/personal/Tom_Fawcett/ROCCH
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