Analysis of the Possibility to Employ Relationship Between the Problem Complexity and the Classification Quality as Model Optimization Proxy

Author:

Komorniczak JoannaORCID,Ksieniewicz PawełORCID,Woźniak MichałORCID

Publisher

Springer Nature Switzerland

Reference21 articles.

1. Barella, V.H., Garcia, L.P., de Souto, M.C., Lorena, A.C., de Carvalho, A.C.: Assessing the data complexity of imbalanced datasets. Inf. Sci. 553, 83–109 (2021)

2. Bartz, E., Zaefferer, M., Mersmann, O., Bartz-Beielstein, T.: Experimental investigation and evaluation of model-based hyperparameter optimization. arXiv preprint arXiv:2107.08761 (2021)

3. Camacho-Urriolagoitia, F.J., Villuendas-Rey, Y., López-Yáñez, I., Camacho-Nieto, O., Yáñez-Márquez, C.: Correlation assessment of the performance of associative classifiers on credit datasets based on data complexity measures. Mathematics 10(9), 1460 (2022)

4. Costa, A.J., Santos, M.S., Soares, C., Abreu, P.H.: Analysis of imbalance strategies recommendation using a meta-learning approach. In: 7th ICML workshop on automated machine learning (AutoML-ICML2020), pp. 1–10 (2020)

5. Dogo, E.M., Nwulu, N.I., Twala, B., Aigbavboa, C.: Accessing imbalance learning using dynamic selection approach in water quality anomaly detection. Symmetry 13(5), 818 (2021)

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