Threshold Moving Approaches for Addressing the Class Imbalance Problem and their Application to Multi-label Classification

In this paper, simple threshold moving techniques are proposed for the class imbalance problem. Decision thresholds are adjusted to match the class distribution of the training data to that of the predicted outcomes for unseen data. The proposed approaches are applied to multi-label classification wherein each instance may simultaneously belong to more than one label. Experimental results on two data sets confirm that the performance of the well-known binary relevance method can be improved when combined with the proposed threshold moving techniques.