  Traditionally, in supervised machine learning, (a significant) part of the
available data (usually 50% to 80%) is used for training and the rest for
validation. In many problems, however, the data is highly imbalanced in regard
to different classes or does not have good coverage of the feasible data space
which, in turn, creates problems in validation and usage phase. In this paper,
we propose a technique for synthesising feasible and likely data to help
balance the classes as well as to boost the performance in terms of confusion
matrix as well as overall. The idea, in a nutshell, is to synthesise data
samples in close vicinity to the actual data samples specifically for the less
represented (minority) classes. This has also implications to the so-called
fairness of machine learning. In this paper, we propose a specific method for
synthesising data in a way to balance the classes and boost the performance,
especially of the minority classes. It is generic and can be applied to
different base algorithms, e.g. support vector machine, k-nearest neighbour,
deep networks, rule-based classifiers, decision trees, etc. The results
demonstrated that: i) a significantly more balanced (and fair) classification
results can be achieved; ii) that the overall performance as well as the
performance per class measured by confusion matrix can be boosted. In addition,
this approach can be very valuable for the cases when the number of actual
available labelled data is small which itself is one of the problems of the
contemporary machine learning.
