Anzeige
Erschienen in:
2008 | OriginalPaper | Buchkapitel
A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory
verfasst von : Thierry Denœux
Erschienen in: Classic Works of the Dempster-Shafer Theory of Belief Functions
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In this paper, the problem of classifying an unseen pattern on the basis of its nearest neighbors in a recorded data set is addressed from the point of view of Dempster-Shafer theory. Each neighbor of a sample to be classified is considered as an item of evidence that supports certain hypotheses regarding the class membership of that pattern. The degree of support is defined as a function of the distance between the two vectors. The evidence of the
k
nearest neighbors is then pooled by means of Dempster’s rule of combination. This approach provides a global treatment of such issues as ambiguity and distance rejection, and imperfect knowledge regarding the class membership of training patterns. The effectiveness of this classification scheme as compared to the voting and distance-weighted
k
-NN procedures is demonstrated using several sets of simulated and real-world data