Testing Modified Confusion Entropy as Split Criterion for Decision Trees

  1. J. David Nuñez-Gonzalez 11
  2. Sá, Alexander Gonzalo de 1
  3. Manuel Graña 11
  1. 1 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Libro:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Año de publicación: 2019

Páginas: 3-13

Congreso: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipo: Aportación congreso

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Resumen

Confusion Entropy (CEN) has been proposed as a performance measure for classification showing a better discrimination against other metrics. Many works use CEN for other purposes. Recently, an improvement in the definition of CEN has been proposed, a modified CEN (MCEN). The aim of this work is to review a previous work based on a classification tree that uses CEN as a pruning criterion, replacing this criterion with the newly defined MCEN metric.

Fuente de los datos: Dialnet