Multilabel text classification (MLTC) methods require enormous labeled training samples to ensure the model's performance, which involves significant manual labor costs. An alternative to conducting MLTC is to only employ predefined representative words of classes, namely category words, as the weak supervision. In this article, we propose a novel weakly supervised (WS) learning MLTC framework consisting of two parts. First, we propose a novel category word selection method, namely category word selection with significance ranking and crowd-sourcing (Cws-src), which generates confident category words by manually selecting from the topically reranked words using a new TW-ITF weighting scheme, thereby effectively mitigating the noises in pseudolabels by filtering repetitive and less significant terms for each class, leading to improved classification performance. Subsequently, we propose a generic WS learning MLTC method, namely WS multilabel text classification with correlation-aware label propagation (Wmltc-clp), which estimates accurate pseudolabels by propagating them over a text correlation graph. To evaluate the proposed framework, we conduct extensive experiments on nine benchmark datasets, including five sentiment analysis datasets and four prevalent MLTC datasets. The results demonstrate that Cws-src can generate more confident category words and Wmltc-clp can achieve significant improvements over the WS learning baselines. The maximum performance gains of Wmltc-clp over the best WS learning baseline methods reach 0.096, 0.081, 0.075, and 0.02 on Micro- $F1$ , Macro- $F1$ , average precision (AP), and ranking loss (RL) across all benchmark datasets.
Ximing Li, Yiming Wang, Chenglong Hu et al.· IEEE Transactions on Neural...· 0 citations
Multi-Label Learning (MLL) refers to inducing multi-label prediction models from the precisely labeled training dataset. However, in many real-world scenarios, e.g., crowdsourcing annotations, the training datasets are often only partially valid, where each training instance is associated with a candidate label set, covering ground-truth labels but also with irrelevant ones. Naturally, learning with such datasets, formally referred to as Partial Multi-label Learning (PML), involves many noisy supervised signals, hence imposing a significant challenge to the prediction model induction. To meet this challenge, we purify the noisy supervised signals by formulating the latent label distribution, i.e., the probability of a candidate label being a ground-truth one, and then jointly learn it with the prediction model by minimizing their regularized Wasserstein distance, i.e., a robust distance for distributions as well as involving label correlations. Therefore, we propose a novel PML method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (Wpml3cp), solved by the gradient descent with an augmented Lagrange multiplier technique. To further enhance the robustness of Wpml3cp against exceptionally high ratios of irrelevant labels, we extend it with a Dual-branch Competitive Cleansing mechanism, leading to Wpml3cp-D. Besides, we also analyze the generalization error bound and time complexity of Wpml3cp and Wpml3cp-D. The extensive experiments are constructed by comparing Wpml3cp and Wpml3cp-D with existing PML baselines across synthetic and real-world datasets, and empirical results demonstrate that Wpml3cp and Wpml3cp-D can outperform the PML baselines in various noisy levels.
Ximing Li, Yuanchao Dai, Bing Wang et al.· ACM Transactions on Knowledg...· 0 citations
A novel framework, Generate and Filter graph learning for Graph Anomaly Detection (GFGAD), which generates a diverse set of synthetic anomalies with enriched feature and structural information to balance the data distribution and significantly outperforms state-of-the-art baselines.
Mengyu Li, Yonghao Liu, Ximing Li et al.· IEEE Transactions on Pattern...· 0 citations