A positive-biased nearest neighbour algorithm for imbalanced classification

Zhang, X and Yuxuan, L 2013, 'A positive-biased nearest neighbour algorithm for imbalanced classification', Lecture Notes in Computer Science, vol. 7819, pp. 293-304.

Document type: Journal Article
Collection: Journal Articles

Title A positive-biased nearest neighbour algorithm for imbalanced classification
Author(s) Zhang, X
Yuxuan, L
Year 2013
Journal name Lecture Notes in Computer Science
Volume number 7819
Start page 293
End page 304
Total pages 12
Publisher Springer
Abstract The k nearest neighbour (kNN) algorithm classifies a query instance to the most frequent class among its k nearest neighbours in the training instance space. For imbalanced class distribution where positive training instances are rare, a query instance is often overwhelmed by negative instances in its neighbourhood and likely to be classified to the negative majority class. In this paper we propose a Positive-biased Nearest Neighbour (PNN) algorithm, where the local neighbourhood of query instances is dynamically formed and classification decision is carefully adjusted based on class distribution in the local neighbourhood. Extensive experiments on real-world imbalanced datasets show that PNN has good performance for imbalanced classification. PNN often outperforms recent kNN-based imbalanced classification algorithms while significantly reducing their extra computation cost.
Subject Pattern Recognition and Data Mining
Keyword(s) imbalanced classification
nearest neighbour classification
Copyright notice © 2013 Springer-Verlag
ISSN 0302-9743
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