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Pattern Analysis and Applications

01-09-2008 | Theoretical Advances

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

Authors: V. García, R. A. Mollineda, J. S. Sánchez

Published in: Pattern Analysis and Applications | Issue 3-4/2008

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Abstract

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.

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Metadata
Title
On the k-NN performance in a challenging scenario of imbalance and overlapping
Authors
V. García
R. A. Mollineda
J. S. Sánchez
Publication date
01-09-2008
Publisher
Springer-Verlag
Published in
Pattern Analysis and Applications / Issue 3-4/2008
Print ISSN: 1433-7541
Electronic ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-007-0087-5

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