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On the Influence of Interval Normalization in IVOVO Fuzzy Multi-class Classifier

  • Mikel UrizEmail author
  • Daniel Paternain
  • Humberto Bustince
  • Mikel Galar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1000)

Abstract

IVOVO stands for Inverval-Valued One-Vs-One and is the combination of IVTURS fuzzy classifier and the One-Vs-One strategy. This method is designed to improve the performance of IVTURS in multi-class problems, by dividing the original problem into simpler binary ones. The key issue with IVTURS is that interval-valued confidence degrees for each class are returned and, consequently, they have to be normalized for applying a One-Vs-One strategy. However, there is no consensus on which normalization method should be used with intervals. In IVOVO, the normalization method based on the upper bounds was considered as it maintains the admissible order between intervals and also the proportion of ignorance, but no further study was developed. In this work, we aim to extend this analysis considering several normalizations in the literature. We will study both their main theoretical properties and empirical performance in the final results of IVOVO.

Notes

Acknowledgment

This work has been partially supported by the Spanish Ministry of Science and Technology under the project TIN2016-77356-P and the Public University of Navarre under the project PJUPNA13.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mikel Uriz
    • 1
    • 2
    Email author
  • Daniel Paternain
    • 1
    • 2
  • Humberto Bustince
    • 1
    • 2
  • Mikel Galar
    • 1
    • 2
  1. 1.Department of Statistics, Computer Science and MathematicsPublic University of NavarrePamplonaSpain
  2. 2.Institute of Smart CitiesPublic University of NavarrePamplonaSpain

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