Enhancing Confusion Entropy (CEN) for binary and multiclass classification

Rosario Delgado, J. David Núñez-González

Research output: Contribution to journalArticleResearch

6 Citations (Scopus)

Abstract

© 2019 Delgado, Núñez-González. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Different performance measures are used to assess the behaviour, and to carry out the comparison, of classifiers in Machine Learning. Many measures have been defined on the literature, and among them, a measure inspired by Shannon’s entropy named the Confusion Entropy (CEN). In this work we introduce a new measure, MCEN, by modifying CEN to avoid its unwanted behaviour in the binary case, that disables it as a suitable performance measure in classification. We compare MCEN with CEN and other performance measures, presenting analytical results in some particularly interesting cases, as well as some heuristic computational experimentation.
Original languageEnglish
Article number0210264
Pages (from-to)e0210264
Number of pages30
JournalPLoS ONE
Volume14
Issue number1
DOIs
Publication statusPublished - 14 Jan 2019

Keywords

  • Algorithms
  • Breast Neoplasms/classification
  • Computational Biology/methods
  • Entropy
  • Female
  • Gene Expression Profiling
  • Humans
  • Machine Learning
  • Models, Biological

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