On Using Asymmetry Information for Classification in Extended Dissimilarity Spaces
When asymmetric dissimilarity measures arise, asymmetry correction methods such as averaging are used in order to make the matrix symmetric. This is usually needed for the application of pattern recognition procedures, but in this way the asymmetry information is lost. In this paper we present a new approach to make use of the asymmetry information in dissimilarity spaces. We show that taking into account the asymmetry information improves classification accuracy when a small number of prototypes is used to create an extended asymmetric dissimilarity space. If the degree of asymmetry is higher, improvements in classification accuracy are also higher. The symmetrization by averaging also works well in general, but decreases performance for highly asymmetric data.
Keywordsasymmetric dissimilarity dissimilarity spaces prototype selection extended dissimilarity spaces