  Learning from class imbalanced datasets poses challenges for many machine
learning algorithms. Many real-world domains are, by definition, class
imbalanced by virtue of having a majority class that naturally has many more
instances than its minority class (e.g. genuine bank transactions occur much
more often than fraudulent ones). Many methods have been proposed to solve the
class imbalance problem, among the most popular being oversampling techniques
(such as SMOTE). These methods generate synthetic instances in the minority
class, to balance the dataset, performing data augmentations that improve the
performance of predictive machine learning (ML) models. In this paper we
advance a novel data augmentation method (adapted from eXplainable AI), that
generates synthetic, counterfactual instances in the minority class. Unlike
other oversampling techniques, this method adaptively combines exist-ing
instances from the dataset, using actual feature-values rather than
interpolating values between instances. Several experiments using four
different classifiers and 25 datasets are reported, which show that this
Counterfactual Augmentation method (CFA) generates useful synthetic data points
in the minority class. The experiments also show that CFA is competitive with
many other oversampling methods many of which are variants of SMOTE. The basis
for CFAs performance is discussed, along with the conditions under which it is
likely to perform better or worse in future tests.
