Toward an Explanatory Similarity Measure for Nearest-Neighbor Classification.

In this paper, a new similarity measure for nearest-neighbor classification is introduced. This measure is an approximation of a theoretical similarity that has some interesting properties. In particular, this latter is a step toward a theory of concepts formation. It renders identical some examples that have distinct representations. Moreover, these examples share some properties relevant for the concept undertaken. Hence, a rule-based representation of the concept can be inferred from the theoretical similarity. Moreover, in this paper, the approximation is validated by some preliminary experiments on non-noisy datasets. [ABSTRACT FROM AUTHOR]