# Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning Source: [Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:20:27.449Z Authors: Ming-Kun Xie, Jia-Hao Xiao, Hao-Zhe Liu, Gang Niu, Masashi Sugiyama, Sheng-Jun Huang Published: 2023-05-04T12:52:18.000Z ## Abstract Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional pseudo-labeling methods encounter difficulties when dealing with instances associated with multiple labels and an unknown label count. These limitations often result in the introduction of false positive labels or the neglect of true positive ones. To overcome these challenges, this paper proposes a novel solution called Class-Aware Pseudo-Labeling (CAP) that performs pseudo-labeling in a class-aware manner. The proposed approach introduces a regularized learning framework incorporating class-aware thresholds, which effectively control the assignment of positive and negative pseudo-labels for each class. Notably, even with a small proportion of labeled examples, our observations demonstrate that the estimated class distribution serves as a reliable approximation. Motivated by this finding, we develop a class-distribution-aware thresholding strategy to ensure the alignment of pseudo-label distribution with the true distribution. The correctness of the estimated class distribution is theoretically verified, and a generalization error bound is provided for our proposed method. Extensive experiments on multiple benchmark datasets confirm the efficacy of CAP in addressing the challenges of SSMLL problems. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multi-label Text Classification on Ohsumed](): Multi-label Text Classification; ohsumed; Micro-F1: 64.5. - [Multi-label Text Classification on RCV1 V2](): Multi-label Text Classification; RCV1 v2; Micro-F1: 77.4. - [Multi-label Text Classification on AAPD](): Multi-label Text Classification; AAPD; Micro-F1: 62.7. ## Original sources and implementation [Original paper]() [PDF]()