SCALA: Scaling Algorithm for Multi-Class Imbalanced Classification

Most of the existing techniques for solving data imbalance problems are geared towards binary classification problems, hence a novel strategy capable of natively handling multi-class classification problems is required. Existing implementations mainly employ a one-versus-rest approach to support multi-class problems and this generalisation hinders its effectiveness in datasets with multiple minority classes. On the contrary, a one-versus-one approach avoids such generalisation and provides finer control over the balancing strategy. In this paper, we propose a novel SCALA algorithm capable of handling imbalanced data with multiple minority class labels with a multi-class output. We introduce a user-defined set of scaling factors which are then integrated with a one-versus-one balancing strategy. Our results show that SCALA demonstrated a significant improvement compared to ADASYN and SMOTE in model performance metrics used to validate balancing techniques. SCALA can balance these datasets without allowing minority classes to overshadow other minority classes. This preserves the information needed by the training algorithm to distinguish between the classes to a high precision.