ABC-sampling for Balancing Imbalanced Datasets Based on Artificial Bee Colony Algorithm

Abstract

Class imbalanced data is a common problem for predictive modelling in domains such as bioinformatics. It occurs when the distribution of classes is not uniform among samples and results in a biased prediction of learning towards majority classes. In this study, we propose the ABC-Sampling algorithm based on a swarm optimization method called Artificial Bee Colony, which models the natural foraging behaviour of honeybees. Our algorithm lessens the effects of imbalanced classes by selecting the most informative majority samples using a forward search and storing them in a ranked subset. Then we construct a balanced dataset with a planned undersampling strategy to extract the most frequent majority samples from the top ranked subset and combine them with all minority samples. Our algorithm is superior to a state-of-the-art method on nine benchmark datasets with various levels of imbalance ratios.