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Proceedings Paper

Evaluating the fusion of multiple classifiers via ROC curves
Author(s): Justin M. Hill; Mark E. Oxley; Kenneth W. Bauer
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Paper Abstract

Given a finite collection of classifiers one might wish to combine, or fuse, the classifiers in hopes that the multiple classifier system (MCS) will perform better than the individuals. One method of fusing classifiers is to combine their final decision using Boolean rules (e.g., a logical OR, AND, or a majority vote of the classifiers in the system). An established method for evaluating a classifier is measuring some aspect of its Receiver Operating Characteristic (ROC) curve, which graphs the trade-off between the conditional probabilities of detection and false alarm. This work presents a unique method of estimating the performance of an MCS in which Boolean rules are used to combine individual decisions. The method requires performance data similar to the data available in the ROC curves for each of the individual classifiers, and the method can be used to estimate the ROC curve for the entire system. A consequence of this result is that one can save time and money by effectively evaluating the performance of an MCS without performing experiments.

Paper Details

Date Published: 25 August 2003
PDF: 12 pages
Proc. SPIE 5096, Signal Processing, Sensor Fusion, and Target Recognition XII, (25 August 2003); doi: 10.1117/12.487493
Show Author Affiliations
Justin M. Hill, Air Force Institute of Technology (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5096:
Signal Processing, Sensor Fusion, and Target Recognition XII
Ivan Kadar, Editor(s)

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