DEI-DEO Fusion as a tool for improving decision robustness across metrics in similarity measure based classifiers

摘要

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.