Relating instance hardness to classification performance in a dataset: a visual approach

Author:

Paiva Pedro Yuri ArbsORCID,Moreno Camila Castro,Smith-Miles Kate,Valeriano Maria Gabriela,Lorena Ana Carolina

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Australian Research Council

Fundação de Amparo à Pesquisa do Estado de São Paulo

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference43 articles.

1. Arruda, J. L., Prudêncio, R. B., & Lorena, A. C. (2020). Measuring instance hardness using data complexity measures. In Brazilian Conference on Intelligent Systems, Springer, pp 483–497.

2. Barek, M. A., Aziz, M. A., & Islam, M. S. (2020). Impact of age, sex, comorbidities and clinical symptoms on the severity of covid-19 cases: A meta-analysis with 55 studies and 10014 cases. Heliyon, 6(12), e05684.

3. Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. In Proceedings of the 24th International Conference on Neural Information Processing Systems, Curran Associates Inc., Red Hook, NY, USA, NIPS’11, p 2546–2554.

4. Bergstra, J., Yamins, D., & Cox, D. D. (2013). Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Proc. 30th International Conference on International Conference on Machine Learning - Volume 28, p I–115–I–123.

5. Böken, B. (2021). On the appropriateness of platt scaling in classifier calibration. Information Systems, 95, 101641.

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