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.
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Latourrette, M. (2000). Toward an Explanatory Similarity Measure for Nearest-Neighbor Classification. In: López de Mántaras, R., Plaza, E. (eds) Machine Learning: ECML 2000. ECML 2000. Lecture Notes in Computer Science(), vol 1810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45164-1_25
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DOI: https://doi.org/10.1007/3-540-45164-1_25
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