Effectiveness of data resampling and ensemble learning in multiclass imbalance learning

Abstract

Classification tasks in many real-world problems often involve multiclass datasets with imbalanced class distributions, which have more difficulty factors than binary classification. Previous studies have proposed various methods to address this multiclass imbalanced learning issue. Data resampling and ensemble learning are the most popular among the proposed methods. However, no comprehensive review or survey has provided an in-depth comparison of ad hoc methods in multiclass imbalance learning, particularly with a focus on data resampling and ensemble learning. Moreover, there is a lack of studies that analyze the effectiveness of each method in terms of the difficulty factors in multiclass imbalance learning. This paper provides a comprehensive review and comparative analysis to identify the strengths and weaknesses of each method and assess their effectiveness in improving classification performance. The analysis shows that not all methods effectively enhance classification performance on multiclass imbalanced datasets. Some methods even perform worse than the baseline performance. The review also reveals that datasets with certain difficulty factors are more challenging for most existing methods to handle. Ultimately, this paper summarizes several important lessons and identifies research gaps to guide future work in the field.