CO\(^2\)RBFN-CS: First Approach Introducing Cost-Sensitivity in the Cooperative-Competitive RBFN Design

  • María Dolores Pérez-GodoyEmail author
  • Antonio Jesús Rivera
  • Francisco Charte
  • Maria Jose del Jesus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)


The interest in dealing with imbalanced datasets has grown in the last years, since they represent many real world scenarios. Different methods that address imbalance problems can be classified into three categories: data sampling, algorithmic modification and cost-sensitive learning. The fundamentals of the last methodology is to assign higher costs to wrong classification classes with the aim of reducing higher cost errors.

In this paper, CO\(^2\)RBFN-CS, a cooperative-competitive Radial Basis Function Network algorithm that implements cost-sensitivity is presented. Specifically, a cost parameter is introduced in the training stage of the algorithm. This parameter modifies the learning rate of the weights taking into account the right (or wrong) classification of the instance, the type of class (majority or minority) and the imbalance ratio of the data set.


RBFNs Imbalanced data sets Cost sensitive 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • María Dolores Pérez-Godoy
    • 1
    Email author
  • Antonio Jesús Rivera
    • 1
  • Francisco Charte
    • 2
  • Maria Jose del Jesus
    • 1
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Computer Science and Artificial InteligenceUniversity of GranadaGranadaSpain

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