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A review of performance evaluation measures for hierarchical classifiers

Costa, Eduardo P. and Lorena, Ana C. and Carvalho, André C.P.L.F. and Freitas, Alex A. (2007) A review of performance evaluation measures for hierarchical classifiers. In: Drummond, Colin and Elazmeh, W. and Japkowicz, N. and Macskassy, S.A., eds. Proceedings of the 2007 AAAI Workshop Evaluation Methods for Machine Learning II. Association for the Advancement of Artificial Intelligence, pp. 1-6. ISBN 978-1-57735-332-4. (KAR id:14562)


Criteria for evaluating the performance of a classifier are an important part in its design. They allow to estimate the behavior of the generated classifier on unseen data and can be also used to compare its performance against the performance of classifiers generated by other classification algorithms. There are currently several performance measures for binary and flat classification problems. For hierarchical classification problems, where there are multiple classes which are hierarchically related, the evaluation step is more complex. This paper reviews the main evaluation metrics proposed in the literature to evaluate hierarchical classification models.

Item Type: Book section
Uncontrolled keywords: data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04 UTC
Last Modified: 10 Oct 2023 11:59 UTC
Resource URI: (The current URI for this page, for reference purposes)

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