JUCS - Journal of Universal Computer Science 26(6): 698-719, doi: 10.3897/jucs.2020.037

Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data

Oscar Camacho-Nieto^‡, Cornelio Yáñez-Márquez^§, Yenny Villuendas-Rey^‡

‡ CIDETEC-IPN, Ciudad de Mexico, Mexico§ CIC-IPN, Ciudad de Mexico, Mexico

Corresponding author: Oscar Camacho-Nieto ( oscarc@cic.ipn.mx )

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.

Citation: Camacho-Nieto O, Yáñez-Márquez C, Villuendas-Rey Y (2020) Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data. JUCS - Journal of Universal Computer Science 26(6): 698-719. https://doi.org/10.3897/jucs.2020.037

Abstract

This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data. The numerical experiments carried out show the proposed undersampling algorithm outperforms others algorithms of the state of art, in well-known imbalanced datasets.

Keywords

undersampling, imbalanced data, hybrid and incomplete data