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Proceedings Paper

Quantifying the correlation effects of fused classifiers
Author(s): Christine M. Schubert; Nathan J. Leap; Mark E. Oxley; Kenneth W. Bauer
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Paper Abstract

Typically, when considering multiple classifiers, researchers assume that they are independent. Under this assumption estimates for the performance of the fused classifiers are easier to obtain and quantify mathematically. But, in fact, classifiers may be correlated, thus, the performance of the fused classifiers will be over-estimated. This paper will address the issue of the dependence between the classifiers to be fused. Specifically, we will assume a level of dependence between two classifiers for a given fusion rule and produce a formula to quantify the performance of this newly fused classifier. The performance of the fused classifiers will then be evaluated via the Receiver Operating Characteristic (ROC) curve. A classifier typically relies on parameters that may vary over a given range. Thus, the probability of true and false positives can be computed over this range of values. The graph of these probabilities over this range then produces the ROC curve. The probability of true positive and false positive from the fused classifiers are developed according to various decision rules. Examples of dependent fused classifiers will be given for various levels of dependency and multiple decision rules.

Paper Details

Date Published: 9 August 2004
PDF: 11 pages
Proc. SPIE 5429, Signal Processing, Sensor Fusion, and Target Recognition XIII, (9 August 2004); doi: 10.1117/12.542578
Show Author Affiliations
Christine M. Schubert, Air Force Institute of Technology (United States)
Nathan J. Leap, 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. 5429:
Signal Processing, Sensor Fusion, and Target Recognition XIII
Ivan Kadar, Editor(s)

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