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Improving the Performance of FARC-HD in Multi-class Classification Problems Using the One-Versus-One Strategy and an Adaptation of the Inference System

  • Mikel Elkano
  • Mikel Galar
  • José Sanz
  • Edurne Barrenechea
  • Francisco Herrera
  • Humberto Bustince
Part of the Communications in Computer and Information Science book series (CCIS, volume 444)

Abstract

In this work we study the behavior of the FARC-HD method when addressing multi-class classification problems using the One-vs-One (OVO) decomposition strategy. We will show that the confidences provided by FARC-HD (due to the use of the product in the inference process) are not suitable for this strategy. This problem implies that robust strategies like the weighted vote obtain poor results. For this reason, we propose two improvements: 1) the replacement of the product by greater aggregations whose output is independent of the number of elements to be aggregated and 2) the definition of a new aggregation strategy for the OVO methodology, which is based on the weighted vote, in which we only take into account the confidence of the predicted class in each base classifier. The experimental results show that the two proposed modifications have a positive impact on the performance of the classifier.

Keywords

Classification One-vs-One Fuzzy Rule-Based Classification Systems Aggregations 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mikel Elkano
    • 1
  • Mikel Galar
    • 1
  • José Sanz
    • 1
  • Edurne Barrenechea
    • 1
  • Francisco Herrera
    • 2
  • Humberto Bustince
    • 1
  1. 1.Dpto. de Automática y ComputaciónUniversidad Publica de NavarraPamplonaSpain
  2. 2.Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology)University of GranadaGranadaSpain

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