Abstract
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.
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Acknowledgments
The work in this paper has been partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and projects KK-2018/00071 and KK-2018/00082 of the Elkartek 2018 funding program of the Basque Government.
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Nuñez-Gonzalez, J.D., Sá, A.G.d., Graña, M. (2019). Testing Modified Confusion Entropy as Split Criterion for Decision Trees. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_1
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