Enhancing Confusion Entropy (CEN) for binary and multiclass classification
Delgado de la Torre, Rosario (Universitat Autònoma de Barcelona. Departament de Matemàtiques)
Nuñez Gonzalez, Jose David (Universidad del País Vasco. Departamento de Matemáticas)

Date:	2019
Abstract:	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.
Grants:	Ministerio de Economía y Competitividad MTM2015-67802-P
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Algorithms ; Breast Neoplasms ; Computational Biology ; Entropy ; Female ; Gene Expression Profiling ; Humans ; Machine Learning ; Models, Biological
Published in:	PloS one, Vol. 1, Issue 1 (January 2019) , p. e0210264, ISSN 1932-6203

DOI: 10.1371/journal.pone.0210264
PMID: 30640948

30 p, 2.8 MB

The record appears in these collections:
Articles > Research articles
Articles > Published articles