Journal
INTERNATIONAL JOINT CONFERENCE SOCO'18-CISIS'18- ICEUTE'18
Volume 771, Issue -, Pages 79-89Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-94120-2_8
Keywords
Classifier; Performance measure; Confusion Entropy (CEN)
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Funding
- Ministerio de Economia y Competitividad, Gobierno de Espana [MTM2015 67802-P]
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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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