Research Interests
- Weakly Supervised Learning
- Partial Multi-label Learning
- Feature Selection
Publications
-
Robust Multi-View Multi-Label Learning via Uncertainty-Gated Diffusion and Dynamic Graph mRMR.
Yanqiang Tu
, Gengyu Lyu, Dian Zhang, Xiaozhu Jing, Wei Ke, Wuman Luo*.
Proceedings of the 32nd ACM SIGKDD
Conference on Knowledge Discovery and Data Mining (KDD).
Incomplete Multi-view Noisy Multi-label Learning (IMvNML), which involves both missing views and
noisy labels, has attracted increasing attention in real-world applications. Existing IMvNML methods
have made progress but still face two key issues: (i) They mainly focus on deterministic view
completion but overlook the inherent generation uncertainty, leading to the introduction of
unreliable reconstructions into latent representations. (ii) They primarily utilize static
topological structures for label correction but overlook the dynamic feedback from refined features,
preventing the joint improvement of feature robustness and label reliability. To address these
issues, we propose a unified framework, named U-DMR, to establish a virtuous cycle via
uncertainty-gated feature selection and closed-loop label refinement. Specifically, we introduce a
Fast Adversarial Diffusion (FAD) method integrated with an Uncertainty-Gated mRMR (UG-mRMR) strategy
to provide reliability-aware view completion and robust feature selection by reducing the influence
of unreliable imputed features. Then, we design a Dynamic Graph Label Refinement (DGLR) method to
improve label reliability by leveraging the robust features to build an evolving semantic graph. The
refined labels provide cleaner supervision, which in turn iteratively improves feature learning.
Extensive experiments on seven benchmark datasets demonstrate that U-DMR consistently outperforms
state-of-the-art methods.
@inproceedings{10.1145/3770855.3817922,
author = {Tu, Yanqiang and Lyu, Gengyu and Zhang, Dian and Jing, Xiaozhu and Ke, Wei and Luo,
Wuman},
title = {Robust Multi-View Multi-Label Learning via Uncertainty-Gated Diffusion and Dynamic Graph
mRMR},
year = {2026},
isbn = {9798400722592},
publisher = {Association for Computing Machinery},
doi = {10.1145/3770855.3817922},
booktitle = {Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining
V.2},
pages = {4683–4694},
series = {KDD '26}
}
-
Dual feature-driven approach for partial multi-label learning.
Yanqiang Tu
, Gengyu Lyu, Wei Ke, Wenbin Qian, Wuman Luo*.
Pattern Recognition (PR).
Partial multi-label learning (PML) addresses the challenge of instances annotated with multiple
candidate labels, of which only a subset are valid. Existing PML methods primarily focus on label
disambiguation through label relevance or label propagation techniques, yet overlook the critical
impact of ambiguous features that may misguide model training. This paper first establishes a
generalization error bound via Rademacher complexity, theoretically proving that ambiguous features
linearly increase model complexity. Motivated by this, we propose a Dual Feature-driven PML method
(DF-PML), which introduces two key innovations: (1) a noise feature identification mechanism based
on low-rank and sparse assumptions combined with matrix decomposition techniques. It aims to
eliminate the interference of noisy features, which is crucial since the presence of noisy features
can obscure the identification of ambiguous features; and (2) an ambiguous feature recognition
mechanism. It employs a shallow network to estimate the ambiguity probability of each feature and
incorporates label correlations and neighborhood granularity to compute label confidence.
Consequently, the impact of ambiguous features is mitigated, leading to improved model performance.
Experimental results show that the proposed DF-PML method outperforms existing methods regarding
overall performance.
@article{TU2026114433,
title = {Dual feature-driven approach for partial multi-label learning},
journal = {Pattern Recognition},
pages = {114433},
year = {2026},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2026.114433},
url = {https://www.sciencedirect.com/science/article/pii/S0031320326013981},
author = {Yanqiang Tu and Gengyu Lyu and Wei Ke and Wenbin Qian and Wuman Luo},
}
-
Partial multi-label learning via three-way decision-based tri-training.
Wenbin Qian*†, Yanqiang Tu†
, Jin Qian
, Wenhao Shu.
Knowledge-Based Systems (KBS).
In real-world application scenarios, multi-label learning (MLL) datasets often contain some
irrelevant noisy labels, which degrades the performance of traditional multi-label learning models.
In order to deal with this problem, partial multi-label learning (PML) is proposed, in which each
instance is associated with a candidate label set, which includes multiple relevant ground-truth
labels and some irrelevant noisy labels. The common strategy to deal with this problem is
disambiguating the candidate label set, but the co-occurrence of noisy labels and ground-truth
labels makes the disambiguation technique susceptible to error. In this paper, a novel
disambiguation-free PML approach named PML-TT is proposed. Specifically, by adapting the
tri-training framework, mutual cooperation and iteration between classifiers are used to correct
noisy labels and improve the performance of the learning model. Moreover, the three-way decision is
adapted to solve the conflict problem of the base classifier and obtain more useful training
samples. In addition, the precise supervisory information of the non-candidate labels is exploited
to make the predictions of the base classifier more accurate. Finally, experimental results on both
synthetic and real-world PML datasets show that the proposed PML-TT approach can effectively reduce
the negative influence of noisy labels and learn a robust model.
@article{qian2023partial,
title={Partial multi-label learning via three-way decision-based tri-training},
author={Qian, Wenbin and Tu, Yanqiang and Qian, Jin and Shu, Wenhao},
journal={Knowledge-Based Systems},
pages={110743},
year={2023},
publisher={Elsevier}
}
-
Partial Multi-Label Learning Using Noise-tolerant Broad Learning System with Label Enhancement and
Dimensionality Reduction.
Wenbin Qian*,
Yanqiang Tu
, Jintao Huang
, Wenhao Shu.
, Yiu-Ming Cheung.
IEEE Transactions on Neural Networks and
Learning Systems (TNNLS).
Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains an
overcomplete set of candidate labels for each instance with only a valid subset of training data.
Using label enhancement techniques, researchers have computed the probability of a label being
ground truth. However, enhancing labels in the noisy label space makes it impossible for the
existing partial multilabel label enhancement methods to achieve satisfactory results. Besides, few
methods simultaneously involve the ambiguity problem, the feature space’s redundancy, and the
model’s efficiency in PML. To address these issues, this article presents a novel joint partial
multilabel framework using broad learning systems (namely BLS-PML) with three innovative mechanisms:
1) a trustworthy label space is reconstructed through a novel label enhancement method to avoid the
bias caused by noisy labels; 2) a low-dimensional feature space is obtained by a confidence-based
dimensionality reduction method to reduce the effect of redundancy in the feature space; and 3) a
noise-tolerant BLS is proposed by adding a dimensionality reduction layer and a trustworthy label
layer to deal with PML problem. We evaluated it on six real-world and seven synthetic datasets,
using eight state-of-the-art partial multilabel algorithms as baselines and six evaluation metrics.
Out of 144 experimental scenarios, our method significantly outperforms the baselines by about 80%,
demonstrating its robustness and effectiveness in handling partial multilabel tasks.
@ARTICLE{10416802,
author={Qian, Wenbin and Tu, Yanqiang and Huang, Jintao and Shu, Wenhao and Cheung, Yiu-Ming},
journal={IEEE Transactions on Neural Networks and Learning Systems},
title={Partial Multilabel Learning Using Noise-Tolerant Broad Learning System With Label Enhancement
and Dimensionality Reduction},
year={2024},
volume={},
number={},
pages={1-15},
keywords={Noise measurement;Learning systems;Dimensionality reduction;Correlation;Sparse
matrices;Redundancy;Kernel;Broad learning system (BLS);dimensionality reduction;granular
computing;label enhancement;noisy labels;partial multilabel learning (PML)},
doi={10.1109/TNNLS.2024.3352285}}
-
Partial Multi-Label Learning via Robust Feature Selection and Relevance Fusion Optimization.
Wenbin Qian*,
Yanqiang Tu
, Jintao Huang
, Weiping Ding.
Knowledge-Based Systems (KBS).
Partial Multi-Label Learning (PML) is a more practical learning paradigm, in which the labeling
information is ambiguated. Most existing PML algorithms rely on assumptions to resolve ambiguity.
However, these assumptions do not account for the origin of the noise labeling and therefore fail to
address the impact of noise on the learner’s performance at the root. In this paper, we will propose
a PML method jointly granular ballbased robust feature selection and relevance fusion optimization
(PML-GR). Specifically, in the first stage, we construct a granular ball to compute the core-set
with weights and then design a feature importance evaluation function to assign weights to each
feature in the core-set, resulting in a ranking of feature importance for the PML learner; in the
second stage, based on the selected features, a fusion-based objective function is constructed to
compute the label confidence by taking into account the joint effect of the global sample similarity
and local label relevance. Finally, a multi-label prediction model is learned by fitting the
multi-output regressor to the label confidence. The experimental results demonstrate that the
proposed method achieves competitive generalization performance by effective feature selection and
relevance fusion optimization, which can focus more on discriminative features and minimize the
effect of noisy labels during training.
@article{QIAN2024111365,
title = {Partial multi-label learning via robust feature selection and relevance fusion
optimization},
journal = {Knowledge-Based Systems},
volume = {286},
pages = {111365},
year = {2024},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2023.111365},
}
Projects
- Jiangxi Province Postgraduate Innovation Special Fund Project: Research and Application of Noisy Label
Learning Algorithm (YC2022-s390), PI , 2022 - 2024.
Teaching Experience
- COMP2113 Operating Systems, Fall, 2024/2025(TA);
- COMP6135 Big Data Analytics, Fall, 2025/2026(TA);
Honors
- Academic Scholarship, Jiangxi Province Government 2022, 2023
- Second Class Scholarship, Jiangxi Agricultural University 2018, 2019, 2020
- Third Class Scholarship, Jiangxi Agricultural University 2020
- Merit Student Award, Jiangxi Agricultural University 2020
- "ShuWei Cup" - the International Mathematical Contest in Modelling Meritorious
2022
- "Huawei Cup" - the 19th China Postgraduate Mathematical Contest in Modelling.
Third Prize 2022
- "Huawei Cup" - the 20th China Postgraduate Mathematical Contest in Modelling.
Second Prize 2023
- "HuaShu Cup" - the 4th National College Mathematical Contest in Modeling Third
Prize 2023
Biogeraphy
- 2024.09 - now Macao Polytechnic University, Faculty of Applied Sciences,
Ph.D.'s Degree;
- 2021.09 - 2024.07 Jiangxi Agricultural University, Computer Science and
Technology, Master's Degree;
- 2017.09 - 2021.07 Jiangxi Agricultural University, E-Commerce,
Bachelor's Degree;
