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2019 | OriginalPaper | Chapter

Enhancing Confusion Entropy as Measure for Evaluating Classifiers

Authors : Rosario Delgado, J. David Núñez-González

Published in: International Joint Conference SOCO’18-CISIS’18-ICEUTE’18

Publisher: Springer International Publishing

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Abstract

Performance measures are used in Machine Learning to assess the behaviour of classifiers. Many measures have been defined on the literature. In this work we focus on Confusion Entropy (CEN), a measure based in Shannon’s Entropy. We introduce a modification of this measure that overcomes its disadvantages in the binary case that disables it as a suitable measure to compare classifiers. We compare this modification with CEN and other measures, presenting analytical results in some particularly interesting cases, as well as some heuristic computational experimentation.

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Title: Enhancing Confusion Entropy as Measure for Evaluating Classifiers
Authors: Rosario Delgado
J. David Núñez-González
Publisher: Springer International Publishing
Book: International Joint Conference SOCO’18-CISIS’18-ICEUTE’18
Print ISBN: 978-3-319-94119-6

Electronic ISBN: 978-3-319-94120-2

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-319-94120-2_8

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