Improving k nearest neighbor with exemplar generalization for imbalanced classification

Authors:
Yuxuan Li;Xiuzhen Zhang
Affiliations:
School of Computer Science and Information Technology, RMIT University, Melbourne, Australia;School of Computer Science and Information Technology, RMIT University, Melbourne, Australia
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Year:
2011

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Abstract

A k nearest neighbor (kNN) 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 (kENN) classifier is therefore more sensitive to the minority class. Extensive experiments show that kENN significantly improves the performance of kNN and also outperforms popular re-sampling and costsensitive learning strategies for imbalanced classification.