Data Imbalance Solutions Using Pseudo-Cluster and Two-Step Classification Methods

Data imbalance poses a significant challenge in machine learning, especially in binary classification t asks, a s traditional algorithms tend to favor the majority class, often resulting in biased models. This paper proposes two novel clustering-based techniques: Pseudo-Cluster Classification and Pseudo-Cluster Two-Step Classification, designed to improve balance and model generalization for the minority class. By partitioning the majority class into distinct sub-clusters, these methods enhance class representation in the dataset, facilitating improved accuracy. Experimental results indicate that these methods outperform default classification approaches, particularly in terms of Macro F1-Score and MCC.