@inproceedings {pub4054,
	title = {Hyperparameter Optimisation for Improving Classification under Class Imbalance},
	author = {Jiawen Kong AND Wojtek Kowalczyk AND Duc Anh Nguyen AND Stefan Menzel AND Thomas B{\"a}ck},
	year = {2019},
	month = {December},
	abstract = {Although the class-imbalance classification problems have 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 learning data complexity before dealing with imbalanced datasets.
Keywords},
	publisher = {IEEE},
	booktitle = {IEEE Symposium Series on Computational Intelligence}
}