Multi-Label Sampling based on Label Imbalance Rate and Neighborhood Distribution

Abstract

Existing multi-label classification algorithms often assume that label distribution in the training set is balanced, but practical datasets frequently exhibit significant label imbalance. This imbalance affects the learning and generalization performance of the classifiers. To address the problem of label imbalance in multi-label classification, this paper proposes a new synthetic oversampling algorithm, named Multi-Label Synthetic Oversampling based on Label Imbalance Rate and Neighborhood Distribution (MLSIN). This algorithm synthesizes new samples by considering both the imbalance rate of labels and the distribution of samples in their neighborhood, aiming to improve the classifiers performance on minority labels. The rest of this chapter first introduces the evaluation metrics for multi-label classification effectiveness. Then it defines and computes the degree of label imbalance, describes the calculation of imbalance weights, and proposes a sample type correction penalty strategy, detailing the algorithm's process for selecting base and auxiliary samples., and validates the proposed method on public datasets and summarizes the experimental results.