Toward an Ecplanatory Similarity Measure for Nearest-Neighbor Classification

Authors:
Mathieu Latourrette
Affiliations:
-
Venue:
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Year:
2000

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Abstract

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.