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.

提出了一种名为BTDT-MBO算法的方法，用于处理高度不均衡的分子数据集上的数据分类问题，该算法通过整合Merriman-Bence-Osher（MBO）技术和双向转换器，以及距离相关性和决策阈值调整来解决分类算法在不平衡数据集上的性能问题，实验证明该方法表现优于竞争算法，即使类别不平衡比例很高。

基于图的双向Transformer决策阈值调整算法用于类别不平衡的分子数据