  Imbalanced learning is a fundamental challenge in data mining, where there is
a disproportionate ratio of training samples in each class. Over-sampling is an
effective technique to tackle imbalanced learning through generating synthetic
samples for the minority class. While numerous over-sampling algorithms have
been proposed, they heavily rely on heuristics, which could be sub-optimal
since we may need different sampling strategies for different datasets and base
classifiers, and they cannot directly optimize the performance metric.
Motivated by this, we investigate developing a learning-based over-sampling
algorithm to optimize the classification performance, which is a challenging
task because of the huge and hierarchical decision space. At the high level, we
need to decide how many synthetic samples to generate. At the low level, we
need to determine where the synthetic samples should be located, which depends
on the high-level decision since the optimal locations of the samples may
differ for different numbers of samples. To address the challenges, we propose
AutoSMOTE, an automated over-sampling algorithm that can jointly optimize
different levels of decisions. Motivated by the success of
SMOTE~\cite{chawla2002smote} and its extensions, we formulate the generation
process as a Markov decision process (MDP) consisting of three levels of
policies to generate synthetic samples within the SMOTE search space. Then we
leverage deep hierarchical reinforcement learning to optimize the performance
metric on the validation data. Extensive experiments on six real-world datasets
demonstrate that AutoSMOTE significantly outperforms the state-of-the-art
resampling algorithms. The code is at https://github.com/daochenzha/autosmote
