Hybrid Associative Memories for Imbalanced Data Classification: An Experimental Study

  • L. Cleofas-Sánchez
  • V. García
  • R. Martín-Félez
  • R. M. Valdovinos
  • J. S. Sánchez
  • O. Camacho-Nieto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7914)


Hybrid associative memories are based on the combination of two well-known associative networks, the lernmatrix and the linear associator, with the aim of taking advantage of their merits and overcoming their limitations. While these models have extensively been applied to information retrieval problems, they have not been properly studied in the framework of classification and even less with imbalanced data. Accordingly, this work intends to give a comprehensive response to some issues regarding imbalanced data classification: (i) Are the hybrid associative models suitable for dealing with this sort of data? and, (ii) Does the degree of imbalance affect the performance of these neural classifiers? Experiments on real-world data sets demonstrate that independently of the imbalance ratio, the hybrid associative memories perform poorly in terms of area under the ROC curve, but the hybrid associative classifier with translation appears to be the best solution when assessing the true positive rate.


Class Imbalance Associative Memory Neural Network 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • L. Cleofas-Sánchez
    • 1
  • V. García
    • 2
  • R. Martín-Félez
    • 2
  • R. M. Valdovinos
    • 3
  • J. S. Sánchez
    • 2
  • O. Camacho-Nieto
    • 4
  1. 1.Instituto Politécnico NacionalCentro de Investigación en ComputaciónMéxico D.F.México
  2. 2.Institute of New Imaging Technologies, Department of Computer Languages and SystemsUniversitat Jaume ICastellón de la PlanaSpain
  3. 3.Centro Universitario Valle de ChalcoUniversidad Autónoma del Estado de MéxicoValle de ChalcoMéxico
  4. 4.Instituto Politécnico NacionalCentro de Innovación y Desarrollo Tecnológico en CómputoMéxico D.F.México

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