Pattern Analysis and Applications

, Volume 11, Issue 3–4, pp 269–280 | Cite as

On the k-NN performance in a challenging scenario of imbalance and overlapping

  • V. García
  • R. A. Mollineda
  • J. S. Sánchez
Theoretical Advances


A two-class data set is said to be imbalanced when one (minority) class is heavily under-represented with respect to the other (majority) class. In the presence of a significant overlapping, the task of learning from imbalanced data can be a very difficult problem. Additionally, if the overall imbalance ratio is different from local imbalance ratios in overlap regions, the task can become in a major challenge. This paper explains the behaviour of the k-nearest neighbour (k-NN) rule when learning from such a complex scenario. This local model is compared to other machine learning algorithms, attending to how their behaviour depends on a number of data complexity features (global imbalance, size of overlap region, and its local imbalance). As a result, several conclusions useful for classifier design are inferred.


Imbalanced data Nearest neighbour rule Class overlap Local and global learning Overall imbalance ratio Local imbalance ratio 



This work has been partially supported by grants DPI2006-15542 from the Spanish CICYT, CSD2007-00018 from the Spanish Ministry of Science and Education and SEP-2003-C02-44225 from the Mexican CONACyT.


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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  1. 1.Laboratorio de Reconocimiento de PatronesInstituto Tecnológico de Toluca. Av. Tecnológico s/nMetepecMéxico
  2. 2.Departament de Llenguatges i Sistemes InformàticsUniversitat Jaume I. Av. Vicent Sos Baynat s/nCastellóSpain

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