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On the Joint-effect of Class Imbalance and Overlap: a Critical Review by Miriam Seoane Santos; Pedro Henriques Abreu; Nathalie Japkowicz; Alberto Fernández; Carlos Soares; Szymon Wilk; João Santos is a Computer Science article available to read on EtoBox.

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Current research on imbalanced data recognises that class imbalance is aggravated by other data intrinsic characteristics, among which class overlap stands out as one of the most harmful. The combination of these two problems creates a new and difficult scenario for classification tasks and has been discussed in several research works over the past two decades. In this paper, we argue that despite some insightful information can be derived from related research, the joint-effect of class overlap and imbalance is still not fully understood, and advocate for the need to move towards a unified view of the class overlap problem in imbalanced domains. To that end, we start by performing a thorough analysis of existing literature on the joint-effect of class imbalance and overlap, elaborating on important details left undiscussed on the original papers, namely the impact of data domains with different characteristics and the behaviour of classifiers with distinct learning biases. This leads to the hypothesis that class overlap comprises multiple representations, which are important to accurately measure and analyse in order to provide a full characterisation of the problem. Accordingly,

Who reads On the Joint-effect of Class Imbalance and Overlap: a Critical Review?

Author

Miriam Seoane Santos; Pedro Henriques Abreu; Nathalie Japkowicz; Alberto Fernández; Carlos Soares; Szymon Wilk; João Santos

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)