Hyperparameter optimisation for improving classification under class imbalance

Ullah, S.; Wang, H.; Menzel, S.; Sendhoff, S.; Back, T.H.W. (2019)

Article in monograph or in proceedings

Although the class-imbalance classification problem has caught a huge amount of attention, hyperparameter optimisation has not been studied in detail in this field. Both classification algorithms and resampling techniques involve some hyperparameters that can be tuned. This paper sets up several experiments and draws the conclusion that, compared to using default hyperparameters, applying hyperparameter optimisation for both classification algorithms and resampling approaches can produce the best results for classifying the imbalanced datasets. Moreover, this paper shows that data complexity, especially the overlap between classes, has a big impact on the potential improvement that can be achieved through hyperparameter optimisation. Results of our experiments also indicate that using resampling techniques cannot improve the performance for some complex datasets, which further emphasizes the importance of analyzing data complexity before dealing with imbalanced datasets.

• All authors: Ullah, S.; Wang, H.; Menzel, S.; Sendhoff, S.; Back, T.H.W.
• Date: 2019
• Pages: 3072 - 3078
• ISBN (print): 9781728124865
• ISBN (electronic): 9781728124858
• DOI: doi:10.1109/SSCI44817.2019.9002679
• Conference: 2019 IEEE Symposium Series on Computational Intelligence (SSCI)
• Date: 2019-12-06 - 2019-12-09
• Location: Xiamen, China