检索结果-内蒙古大学图书馆

multi-label learning for fault diagnosis of pumping units with one positive label

APPLIED SOFT COMPUTING 2025年 174卷

作者： Qian, Kun Tang, Jinyu Zhao, Qimei Zhao, Shu Min, Fan Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China Chaohu Univ Sch Comp Sci & Artificial Intelligence Hefei 238024 Peoples R China Southwest Petr Univ Lab Machine Learning Chengdu 610500 Peoples R China

Fault diagnosis using the indicator diagram is a fundamental method to evaluate the working status of pumping units. In applications, human experts typically identify only one fault for each indicator diagram. However, multiple types of faults may occur simultaneously. In this paper, we propose a Single-Positive multi-label learning for Fault Diagnosis of Pumping Units (SPM-FDPU) algorithm to address this issue. Although trained on single-label data, it is capable of multi-label prediction. First, HU invariant moments and convolutional neural networks are used to extract common and label-specific features, respectively. Second, instance, feature, and label correlations are injected into the training process by feature and label manifolds to enhance supervised information. Third, the manifold is used to augment the latent label matrix to help explore discriminant information. Experiments are conducted on the three real indicator diagram data of an oil field and sixteen multi-label benchmark datasets. The results show that the accuracy of the proposed method has achieved 98% in diagnosing multiple faults on indicator diagram datasets, and the mean rank of the proposed method is optimal in terms of six popular evaluation metrics on multi-label benchmark datasets. The source code is available at ***/Kqian2020/SPM-FDPU.

关键词： Fault diagnosis Indicator diagram HU invariant moments multi-label learning Single-positive

来源：评论

学校读者我要写书评

暂无评论

Partial multi-label learning via label-specific feature corrections

引用

Science China(Information Sciences) 2025年第3期68卷 95-109页

作者： Jun-Yi HANG Min-Ling ZHANG School of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of Education

Partial multi-label learning（PML） allows learning from rich-semantic objects with inaccurate annotations, where a set of candidate labels are assigned to each training example but only some of them are valid. Existing approaches rely on disambiguation to tackle the PML problem, which aims to correct noisy candidate labels by recovering the ground-truth labeling information ahead of prediction model induction. However, this dominant strategy might be suboptimal as it usually needs extra assumptions that cannot be fully satisfied in real-world scenarios. Instead of label correction, we investigate another strategy to tackle the PML problem, where the potential ambiguity in PML data is eliminated by correcting instance features in a label-specific manner. Accordingly, a simple yet effective approach named PASE, i.e., partial multi-label learning via label-specific feature corrections, is proposed. Under a meta-learning framework, PASElearns to exert label-specific feature corrections so that potential ambiguity specific to each class label can be eliminated and the desired prediction model can be induced on these corrected instance features with the provided candidate labels. Comprehensive experiments on a wide range of synthetic and real-world data sets validate the effectiveness of the proposed approach.

关键词： machine learning multi-label learning partial multi-label learning label-specific features feature correction

来源：评论

学校读者我要写书评

暂无评论

Incorporating view location information for multi-view multi-label learning

引用

APPLIED SOFT COMPUTING 2025年 168卷

作者： Wang, Jiabao Cheng, Yusheng Anqing Normal Univ Sch Comp & Informat Anqing 246133 Anhui Peoples R China Key Lab Intelligent Percept & Comp Anhui Prov Anqing 246133 Anhui Peoples R China

In multi-view multi-label learning (MVML), subspace learning provides an effective solution for integrating multi-view data. However, the current learning concentrates on the shared subspace construction process and neglects the exploration of location information, i.e., the view source of the feature, which is inherent to the feature itself. This has somewhat limited the progress of research on MVML. On this basis, we propose a multiview multi-label learning method based on incorporating view location information (MMVLI) for the first time with reference to the Vision Transformer architecture. Firstly, the common features and special features between views are obtained through the feature extraction. The above two are combined according to the feature dimension and added into the learnable location information encoding matrix named the collaborative matrix. Then, the collaborative matrix is put into Transformer Encoder for self-attention learning to get the final feature matrix for multi-label learning. Extensive comparative experiments with existing state-of-the-art methods on six publicly available multi-view multi-label datasets demonstrate the effectiveness of MMVLI.

关键词： multi-label learning multi-view learning Location information Transformer

来源：评论

学校读者我要写书评

暂无评论

Sandbox: safeguarded multi-label learning through safe optimal transport

引用

MACHINE learning 2025年第3期114卷 1-29页

作者： Zhang, Lefei Yu, Geng Yao, Jiangchao Ong, Yew-soon Tsang, Ivor W. Kwok, James T. Nanyang Technol Univ Coll Comp & Data Sci 50 Nanyang Ave Singapore 639798 Singapore Agcy Sci Technol & Res Ctr Frontier AI Res 1 Fusionopolis Way Singapore 138632 Singapore Agcy Sci Technol & Res Inst High Performance Comp 1 Fusionopolis Way Singapore 138632 Singapore Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China

multi-label learning with label noise presents significant real-world challenges due to dependencies among labels, complicating the transition from clean to noisy labels. Mainstream approaches, such as robust loss functions, and noise transition models, often fall short due to their high sensitivity to noise rate or transition matrix estimation, especially on complex datasets. To address these challenges, we introduce a novel Sandbox mechanism, which iteratively estimates multiple labels. Unlike traditional methods that depend on an explicit, often restrictive linear transition matrix, Sandbox mechanism utilizes an implicit optimal transport process, constraining label refinements between noisy labels and model predictions within a predefined polytope, effectively limiting error propagation, enhancing stability, and offering greater flexibility. In each iteration, we develop a simple yet effective method, termed as Safe Optimal Transport (SOT), to refine noisy labels more reliably towards the ground truth. By involving the interpolated references and complementary orientations, SOT effectively estimates true labels using the Sinkhorn-Knopp algorithm. Our extensive evaluations on various benchmark datasets demonstrate that Sandbox consistently outperforms existing state-of-the-art techniques. Comprehensive ablation studies further elucidate its effectiveness.

关键词： multi-label learning label noise Optimal transport Robust machine learning

来源：评论

学校读者我要写书评

暂无评论

NkEL: nearest k-labelsets ensemble for multi-label learning

引用

APPLIED INTELLIGENCE 2025年第1期55卷 1-21页

作者： Zhong, Xi-Yan Zhang, Yu-Li Wang, Dan-Dong Min, Fan Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China Southwest Petr Univ Lab Machine Learning Chengdu 610500 Peoples R China Southwest Petr Univ Intelligent Oil & Gas Lab Chengdu 610500 Peoples R China

multi-label learning (MLL) can be viewed as an extension of multi-class learning (MCL) that supports nonexclusive labels. Random k-labelset ensemble (RAkEL) is a popular algorithm that transforms MLL into a series of MCL tasks to exploit label correlations. However, its effectiveness is impacted by the randomness of labelset construction. In this paper, we propose an MLL algorithm with nearest k-labelsets ensemble (NkEL) possessing three techniques. First, we select a labelset with a size of k for each label using the nearest-neighbor technique. Thus, NkEL considers high-order label correlations and has strong adaptability. Second, for each MCL problem, we build a neural network to provide numerical rather than categorical predictions. Therefore, the output values represent the confidence levels of different classes. Third, we propose an intra-labelset ensemble strategy for each label. This approach alleviates the limitations imposed by low class separability with the support of the total probability theorem. Experiments are conducted on datasets derived from various domains to compare the proposed method with fourteen popular algorithms. The results obtained in terms of six ranking-based and two classification-based measures demonstrate the feasibility and effectiveness of NkEL. The source code is available at ***/fansmale/nkel.

关键词： Ensemble label correlation multi-label learning Neural network

来源：评论

学校读者我要写书评

暂无评论

Pushing one pair of labels apart each time in multi-label learning: from single positive to full labels

引用

SCIENCE CHINA-INFORMATION SCIENCES 2025年第6期68卷 1-18页

作者： Li, Xiang Wang, Xinrui Chen, Songcan Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Pattern Anal & Machine Intelligence Coll Artificial Intelligence Coll Comp Sci & Technol Nanjing 211106 Peoples R China

In multi-label learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternative should be single positive multi-label learning (SPMLL), where only one positive label needs to be provided per sample. Existing SPMLL methods usually assume unknown labels as negatives, which inevitably introduces false negatives as noisy labels. More seriously, binary cross entropy (BCE) loss is often used for training, which is notoriously not robust to noisy labels. To mitigate this issue, we customize an objective function for SPMLL by pushing only one pair of labels apart each time to suppress the domination of negative labels, which is the main culprit of fitting noisy labels in SPMLL. To further combat such noisy labels, we explore the high-rankness of the label matrix, which can also push apart different labels. By directly extending from SPMLL to MLL with full labels, a unified loss applicable to both settings is derived. As a byproduct, the proposed loss can alleviate the imbalance inherent in MLL. Experiments on real datasets demonstrate that the proposed loss not only performs more robustly to noisy labels for SPMLL but also works well for full labels. Besides, we empirically discover that high-rankness can mitigate the dramatic performance drop in SPMLL. Most surprisingly, even without any regularization or fine-tuned label correction, only adopting our loss defeats state-of-the-art SPMLL methods on CUB, a dataset that severely lacks labels.

关键词： multi-label learning single positive label noisy labels missing labels image classification

来源：评论

学校读者我要写书评

暂无评论

A diversity and reliability-enhanced synthetic minority oversampling technique for multi-label learning

引用

INFORMATION SCIENCES 2025年 690卷

作者： Gong, Yanlu Wu, Quanwang Zhou, Mengchu Chen, Chao Chongqing Univ Technol Sch Artificial Intelligence Chongqing 401135 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400030 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

The class imbalance issue is generally intrinsic in multi-label datasets due to the fact that they have a large number of labels and each sample is associated with only a few of them. This causes the trained multi-label classifier to be biased towards the majority labels. multi-label oversampling methods have been proposed to handle this issue, and they fall into clone-based and Synthetic Minority Oversampling TEchnique-based (SMOTE-based) ones. However, the former duplicates minority samples and may result in over-fitting whereas the latter may generate unreliable synthetic samples. In this work, we propose a Diversity and Reliability-enhanced SMOTE for multi-label learning (DR-SMOTE). In it, the minority classes are determined according to their label imbalance ratios. A reliable minority sample is used as a seed to generate a synthetic one while a reference sample is selected for it to confine the synthesis region. Features of the synthetic samples are determined probabilistically in this region and their labels are set identically to those of the seeds. We carry out experiments with eleven multi-label datasets to compare DR-SMOTE against seven existing resampling methods based on four base multi-label classifiers. The experimental results demonstrate DR-SMOTE's superiority over its peers in terms of several evaluation metrics.

关键词： Class imbalance Diversity multi-label learning Oversampling Reliability

来源：评论

学校读者我要写书评

暂无评论

multi-label learning based on neighborhood rough set label-specific features

引用

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2025年 178卷

作者： Zhang, Jiadong Song, Jingjing Li, Huige Wang, Xun Yang, Xibei Jiangsu Univ Sci & Technol Sch Comp Zhenjiang 212100 Jiangsu Peoples R China

multi-label learning emerges as a novel paradigm harnessing diverse semantic datasets. Its objective involves eliciting a prognostic framework capable of allocating correlated labels to an unseen instance. Within the multifaceted domain of multi-label learning, the adoption of a label- specific feature methodology is prevalent. This approach entails the induction of a classification model that forecasts the relevance of each class label, utilizing tailored features specific to each label rather than relying on the original features. However, some irrelevant or redundant features will inevitably be generated when constructing features. To address this issue, we extend the current approach and introduce a straightforward yet potent multi-label learning method named NRS-LIFT, i.e., Neighborhood Rough Set label-specIfic FeaTures. Specifically, a sample selection method is used to reduce the computational complexity, and then a set of tailored features is customized for each label through the neighborhood rough set. Finally, a learning model is induced to predict unseen instances. To fully evaluate the effectiveness of NRS-LIFT, we conduct extensive experiments on 12 multi-label datasets. Compared with mature multi-label learning methods, it is verified that NRS-LIFT has strong performance for multi-label datasets.

关键词： label-specific feature multi-label learning Neighborhood rough set

来源：评论

学校读者我要写书评

暂无评论

Matrix factorization algorithm for multi-label learning with missing labels based on fuzzy rough set

引用

FUZZY SETS AND SYSTEMS 2025年 498卷

作者： Deng, Jiang Chen, Degang Wang, Hui Shi, Ruifeng North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China North China Elect Power Univ Sch Math & Phys Beijing 102206 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5BN North Ireland

In multi-label learning, samples of practical classification task may associated with multiple labels, it is challenging to acquire all labels of the training samples, the rapid expansion of the label space and the significant increase in annotation costs have exacerbated the issue of missing labels in multi-label learning. By utilizing the low rank structure of the label matrix, missing labels can be effectively recovered with matrix factorization technique. Nevertheless, current approaches disregard the potential correlation between the feature space and the multi-dimensional label data. In this paper, key features associated to different labels are extracted and then a non- negative matrix factorization algorithm is proposed for recover missing labels. Firstly, the fuzzy rough set theory is used to analyze the consistency between label matrix and feature space, potential feature information is employed to determine the latent variable dimension in non- negative matrix decomposition and the symbolic label matrix is also converted to a numerical one by means of the lower approximation operator. Then, feature-based manifold regularization and local label correlations are used to model the multi-label completion algorithm. In order to verify the effectiveness in dealing incomplete label data, comparison experiments with varying levels of missing values are designed, the experiments show that compared with the state-of-theart algorithm, the proposed algorithm is effective in the completion of missing labels. In addition, the sensitivity analysis experiments also show that the proposed method has good stability.

关键词： multi-label learning Fuzzy rough set Incomplete multi-label Non-negative matrix factorization label correlation

来源：评论

学校读者我要写书评

暂无评论

Hierarchical multi-granular multi-label contrastive learning

引用

PATTERN RECOGNITION 2025年 164卷

作者： Li, Haixiang Fang, Min Li, Xiao Chen, Bo Wang, Guizhi Xidian Univ Sch Comp Sci & Technol 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China

multi-label classification is a task wherein predictive models are developed to assign relevant label sets to unseen instances. label correlation extraction and utilization have been widely implemented in multi-label classification methodologies. However, current multi-label contrastive learning algorithms inadequately incorporate label correlations into the feature space, thus limiting the learning of optimal feature representations for multi-label samples. To address this limitation, a novel hierarchical multi-granularity multi-label contrastive learning approach is proposed in this paper. The proposed method encompasses the construction of multi- label hierarchical correlations, label expansion based on hierarchical relationships, and representation learning through multi-granularity contrastive learning built upon these structures. Experimental results demonstrate the superiority of the proposed method over state-of-the-art techniques across widely used datasets.

关键词： multi-label learning Contrastive learning label enhancement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：