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Item response theory in AI: Analysing machine learning classifiers at the instance level

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Item response theory in AI: Analysing machine learning classifiers at the instance level

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Martínez-Plumed, F.; Prudencio, R.; Martínez-Usó, A.; Hernández-Orallo, J. (2019). Item response theory in AI: Analysing machine learning classifiers at the instance level. Artificial Intelligence. 271:18-42. https://doi.org/10.1016/j.artint.2018.09.004

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/139921

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Title: Item response theory in AI: Analysing machine learning classifiers at the instance level
Author: Martínez-Plumed, Fernando Prudencio, Ricardo Martínez-Usó, Adolfo Hernández-Orallo, José
UPV Unit: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Issued date:
Embargo end date: 2021-07-01
Abstract:
[EN] AI systems are usually evaluated on a range of problem instances and compared to other AI systems that use different strategies. These instances are rarely independent. Machine learning, and supervised learning in ...[+]
Subjects: Artificial intelligence evaluation , Item response theory , Machine learning , Instance hardness , Classifier metrics
Copyrigths: Embargado
Source:
Artificial Intelligence. (issn: 0004-3702 )
DOI: 10.1016/j.artint.2018.09.004
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.artint.2018.09.004
Thanks:
This work has been partially supported by the EU (FEDER) and the Ministerio de Economia y Competitividad (MINECO) in Spain grant TIN2015-69175-C4-1-R, the Air Force Office of Scientific Research under award number ...[+]
Type: Artículo

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