Anzeige
Erschienen in:
2011 | OriginalPaper | Buchkapitel
Improving k Nearest Neighbor with Exemplar Generalization for Imbalanced Classification
verfasst von : Yuxuan Li, Xiuzhen Zhang
Erschienen in: Advances in Knowledge Discovery and Data Mining
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
A
k
nearest neighbor (
k
NN) classifier classifies a query instance to the most frequent class of its
k
nearest neighbors in the training instance space. For imbalanced class distribution, a query instance is often overwhelmed by majority class instances in its neighborhood and likely to be classified to the majority class. We propose to identify exemplar minority class training instances and generalize them to Gaussian balls as concepts for the minority class. Our
k
Exemplar-based Nearest Neighbor (
k
ENN) classifier is therefore more sensitive to the minority class. Extensive experiments show that
k
ENN significantly improves the performance of
k
NN and also outperforms popular re-sampling and cost-sensitive learning strategies for imbalanced classification.