Categorical Pseudo-Labeling with Iterative Cluster Expansion

Labeling categorical data is a critical challenge in many real-world applications where expert annotation is costly, time-consuming, and often impractical. Moreover, we very often face the problem of training sets that are too small or unrepresentative to apply supervised learning techniques. Traditional pseudo-labeling techniques, primarily designed for numerical data, struggle to handle the unique characteristics of categorical datasets. To address this, we introduce Categorical Pseudo-Labeling with Iterative Cluster Expansion (CPLICE), a novel clustering-based approach that leverages qualitative similarity measures to iteratively expand labeled datasets. Unlike probability-based pseudo-labeling, CPLICE selects data points based on their distance from established clusters, progressively refining labels while handling ambiguous cases through dynamic reassignments. Our results demonstrate that CPLICE outperforms traditional tree-based models in low-label environments, providing a stable, scalable solution for effective data labeling. This method is particularly valuable for real-world scenarios such as medical diagnostics, customer segmentation, and fraud detection, where robust handling of categorical data is essential for accurate, interpretable models.