Research Interests
- Weakly Supervised Learning
- Partial Multi-label Learning
- Feature Selection
Publications
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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}
}
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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}}
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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);
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;
