A Data Representation Approach to Support Imbalanced Data Classification Based on TWSVM

  • C. JimenezEmail author
  • A. M. Alvarez
  • A. Orozco
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Imbalance classification requires to represent the input data adequately to avoid biased results towards the class with the greater samples number. Here, we introduce an enhanced version of the famous twin support vector machine (TWSVM) classifier by incorporating an extended dual formulation of its quadratic programming optimization. Besides a centered kernel alignment (CKA)-based representation is used to avoid data overlapping. In particular, our approach, termed enhanced TWSVM (ETWSVM), allows representing the input samples in a high dimensional space (possibly infinite) after reformulation of the TWSVM dual form. Obtained results for binary classification demonstrate that our ETWSVM can reveal relevant data structures diminishing overlapping and biased classification results under imbalance scenarios. Moreover, ETWSVM notably adopts the lowest computational cost for training in comparison to state-of-the-art methods.


TWSVM Imabalanced data Kernel enhacenment 



Under grants support by the project: “Desarrollo de un sistema de posorte clínico basado en el procesamiento estócastico para mejorar la resolusión espacial de la resonancia magnética estructural y de difusión con aplicación al procedimiento de la ablación de tumores”, code: 111074455860, funded by COLCIENCIAS. Moreover, C. Jimenez is partially financed by the project E6-18-09: “Clasificador de máquinas de vectores de soporte para problemas desbalanceados con selección automática de parámetros”, funded by Vicerrectoria de Investigación, innovación y extension and by Maestría en Ingeniería Eléctrica, both from Universidad Tecnológica de Pereira.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Automatics Research GroupUniversidad Tecnologica de PereiraPereiraColombia

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