Zhang, Dell and Wang, J. and Zhao, X. (2015) Estimating the uncertainty of average F1 scores. In: UNSPECIFIED (ed.) ICTIR '15: Proceedings of the 2015 International Conference on The Theory of Information Retrieval. New York, U.S.: ACM, pp. 317-320. ISBN 9781450338332.
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Abstract
In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data. In this paper, we propose a novel approach to explicitly modelling the uncertainty of average F1 scores through Bayesian reasoning.
Metadata
| Item Type: | Book Section |
|---|---|
| Keyword(s) / Subject(s): | Text Classification, Performance Evaluation, Bayesian Inference |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for |
| Depositing User: | Dr Dell Zhang |
| Date Deposited: | 11 Dec 2015 15:10 |
| Last Modified: | 09 Aug 2023 12:37 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/13586 |
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