Tackling Diverse Minorities in Imbalanced Classification
Kwei-Herng Lai Daochen Zha Huiyuan Chen Mangesh Bendre Yuzhong Chen Mahashweta Das Hao Yang Xia Hu
Imbalanced datasets are commonly observed in various real-world applications,
presenting significant challenges in training classifiers. When working with
large datasets, the imbalanced issue can be further exacerbated, making it
exceptionally difficult to train classifiers effectively. To address the
problem, over-sampling techniques have been developed to linearly interpolating
data instances between minorities and their neighbors. However, in many
real-world scenarios such as anomaly detection, minority instances are often
dispersed diversely in the feature space rather than clustered together.
Inspired by domain-agnostic data mix-up, we propose generating synthetic
samples iteratively by mixing data samples from both minority and majority
classes. It is non-trivial to develop such a framework, the challenges include
source sample selection, mix-up strategy selection, and the coordination
between the underlying model and mix-up strategies. To tackle these challenges,
we formulate the problem of iterative data mix-up as a Markov decision process
(MDP) that maps data attributes onto an augmentation strategy. To solve the
MDP, we employ an actor-critic framework to adapt the discrete-continuous
decision space. This framework is utilized to train a data augmentation policy
and design a reward signal that explores classifier uncertainty and encourages
performance improvement, irrespective of the classifier's convergence. We
demonstrate the effectiveness of our proposed framework through extensive
experiments conducted on seven publicly available benchmark datasets using
three different types of classifiers. The results of these experiments showcase
the potential and promise of our framework in addressing imbalanced datasets
with diverse minorities.