  Data sets with imbalanced class sizes, often where one class size is much
smaller than that of others, occur extremely often in various applications,
including those with biological foundations, such as drug discovery and disease
diagnosis. Thus, it is extremely important to be able to identify data elements
of classes of various sizes, as a failure to detect can result in heavy costs.
However, many data classification algorithms do not perform well on imbalanced
data sets as they often fail to detect elements belonging to underrepresented
classes. In this paper, we propose the BTDT-MBO algorithm, incorporating
Merriman-Bence-Osher (MBO) techniques and a bidirectional transformer, as well
as distance correlation and decision threshold adjustments, for data
classification problems on highly imbalanced molecular data sets, where the
sizes of the classes vary greatly. The proposed method not only integrates
adjustments in the classification threshold for the MBO algorithm in order to
help deal with the class imbalance, but also uses a bidirectional transformer
model based on an attention mechanism for self-supervised learning.
Additionally, the method implements distance correlation as a weight function
for the similarity graph-based framework on which the adjusted MBO algorithm
operates. The proposed model is validated using six molecular data sets, and we
also provide a thorough comparison to other competing algorithms. The
computational experiments show that the proposed method performs better than
competing techniques even when the class imbalance ratio is very high.
