|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
MAXIMUM REALISABLE PERFORMANCE: A PRINCIPLED METHOD FOR ENHANCING PERFORMANCE BY USING MULTIPLE CLASSIFIERS IN VARIABLE COST PROBLEM DOMAINS
M.J.J. Scott, M. Niranjan, D.G. Melvin, R.W. Prager.
A novel method is described for obtaining superior classification performance over a variable range of classification costs. By analysis of a set of existing classifiers using a receiver operating characteristic (ROC) curve, a set of new realisable classifiers may be obtained by a principled random combination of two of the existing classifiers. These classifiers lie on the convex hull that contains the original $ROC$ points for the existing classifiers. This hull is the maximum realisable ROC (MRROC).
A theorem for this method is derived and proved from an observation about ROC data, and experimental results verify that a superior classification system may be constructed using only the existing classifiers and the information of the original ROC data. This new system is shown to produce the MRROC, and as such provides a powerful technique for improving classification systems in problem domains within which classification costs may not be known a priori.
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