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Proceedings Paper

DEI-DEO fusion as a tool for improving decision robustness across metrics in similarity-measure-based classifiers
Author(s): Belur V. Dasarathy
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Paper Abstract

Decisions derived through similarity measure based classifiers, such as nearest neighbor classifiers, are known to be sensitive to the choice of metrics underlying the similarity measure. Accordingly, it would be advisable to explore means of deriving decisions that are relatively independent of such choice. An approach that suggests itself is to fuse the decisions derived through the different metrics such that the fused decision is independent of the choice of metric and hence more robust. Here, in this study, this "fusion across metrics" concept is developed and illustrated experimentally with examples using multiple data sets employed in the open literature. For illustrative purposes, the choice of metrics is limited to three cases of the Minkowski metric, namely the Manhattan, Euclidean, and the Supremum metrics and a single pre-defined DEI-DEO fusion logic.

Paper Details

Date Published: 12 April 2004
PDF: 12 pages
Proc. SPIE 5434, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, (12 April 2004); doi: 10.1117/12.543584
Show Author Affiliations
Belur V. Dasarathy, Consultant (United States)

Published in SPIE Proceedings Vol. 5434:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2004
Belur V. Dasarathy, Editor(s)

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