
Proceedings Paper
Event induced bias in label fusionFormat | Member Price | Non-Member Price |
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Paper Abstract
In a two class label scenario, classi
cation systems may be used to assess whether or not an element of interest
belongs to the targetor non-targetclass. The performance of the system is summarized visually as a trade-
o¤ between the proportions of elements correctly labeled as targetplotted against the proportion of elements
incorrectly labeled as target. These proportions are empirical estimates of the true and false positive rates,
and their trade-o¤ plot is known as a receiver operating characteristic (ROC) curve. Classi
cation performance
can be increased, however, if the information provided by multiple systems can be fused together to create a
new, combined system. This research focuses on label-fusion as a common method to increase classi
cation
performance and quantifying the bias that occurs when misspecifying the partitioning of the underlying event
set. This partitioning will be de
ned in terms of what be called within and across label fusion. When incorrect
assumptions are made about the partitioning of the event set, bias will occur and both the ROC curve and its
optimal parameters will be incorrectly quanti
ed. In this work, we analyze the e¤ects of individual classi
cation
system performance, correlation, and target environment on the magnitude of this performance bias. This work
will then inspire the development of formulas to adjust optimal performance measures to appropriately reect
the fused system performance according to event set partitioning. As such, bias may be appropriately adjusted
without redesigning the fused system, allowing greater use of currently fused systems across multiple platforms
and environments.
Paper Details
Date Published: 2 May 2017
PDF: 12 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000G (2 May 2017); doi: 10.1117/12.2264632
Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)
PDF: 12 pages
Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000G (2 May 2017); doi: 10.1117/12.2264632
Show Author Affiliations
Christine M. Schubert Kabban, Air Force Institute of Technology (United States)
Alexander M. Venzin, Air Force Institute of Technology (United States)
Alexander M. Venzin, Air Force Institute of Technology (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)
Published in SPIE Proceedings Vol. 10200:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI
Ivan Kadar, Editor(s)
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