Automated Imbalanced Classification via Meta-learning

Abstract Imbalanced learning is one of the most relevant problems in machine learning. However, it faces two crucial challenges. First, the amount of methods proposed to deal with such problem has grown immensely, making the validation of a large set of methods impractical. Second, it requires specialised knowledge, hindering its use by those without such level of experience. In this paper, we propose the Automated Imbalanced Classification method, ATOMIC. Such a method is the first automated machine learning approach for imbalanced classification tasks. It provides a ranking of solutions most likely to ensure an optimal approximation to a new domain, drastically reducing associated computational complexity and energy consumption. We carry this out by anticipating the loss of a large set of predictive solutions in new imbalanced learning tasks. We compare the predictive performance of ATOMIC against state-of-the-art methods using 101 imbalanced data sets. Results demonstrate that the proposed method provides a relevant approach to imbalanced learning while reducing learning and testing efforts of candidate solutions by approximately 95%.