This Special Issue includes original research and reviews of the literature focusing on food labels, which are a tool to promote public health that, at the same time, may represent a marketing tool ...and may influence consumers’ perception of food quality.
The Emerging Trends of Multi-Label Learning Liu, Weiwei; Wang, Haobo; Shen, Xiaobo ...
IEEE transactions on pattern analysis and machine intelligence,
2022-Nov.-1, 2022-11-1, 20221101, Letnik:
44, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme ...multi-label classification is an active and rapidly growing research area that deals with classification tasks with extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there have been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfil this mission and delineate future research directions and new applications.
•An integrated framework to recover missing labels and train the multi-label classifier by learning label correlations and transforming label subspace.•Maximally separated label subspaces for label ...differentiation and a low rank structure capturing label specific subspace.•Modelling global label correlations to learn auxiliary label matrix for missing label recovery.•Experimental evaluations on fourteen multi-label datasets and performance comparison with six state of the art methods prove effectiveness of model.
Multi-label datasets often contain label information with missing values and recovering them is a non-trivial challenge. Several methods augment the observed label matrix by constructing auxiliary labels and learning high order label correlations. Some other techniques exploit the low rank of the label matrix to capture a mix of label correlations. Both these approaches rely on label correlations, however in different ways. In this paper, we propose a unified framework that captures the label correlations utilizing both auxiliary label matrix and the low rank constraints on estimated labels. Our model also enforces maximal separation among different label subspaces for better label differentiation. The proposed method captures local and global correlations using Low Rank label subspace transformation for Multi-label learning with Missing Labels (LRMML). The model considers an auxiliary label matrix which facilitates the missing label information recovery. Low rank on predictions ensures that local label structures are captured and the maximal inter-label subspace separation helps identify discriminatory label correlations. The proposed method builds a multi-label classification model by solving a multivariate difference of convex objective function using surrogate optimization technique and alternating minimization. Empirical results on several benchmark datasets validate the effectiveness of the proposed method against state-of-the-art multi-label learning approaches.
•We develop a simple yet effective discriminant multi-label learning (DM2L) method for multi-label learning with missing labels.•We impose both local and global rank structures to model label ...structures, and also to provide more label discriminability.•We apply the kernel trick to provide a nonlinear extension to enhance nonlinear ability of our model.•Although our method involves the fewest assumptions and only one hyper-parameter, it still outperforms the state-of-the-art methods.
In multi-label learning, the issue of missing labels brings a major challenge. Many methods attempt to recovery missing labels by exploiting low-rank structure of label matrix. However, these methods just utilize global low-rank label structure, ignore both local low-rank label structures and label discriminant information to some extent, leaving room for further performance improvement. In this paper, we develop a simple yet effective discriminant multi-label learning (DM2L) method for multi-label learning with missing labels. Specifically, we impose the low-rank structures on all the predictions of instances from the same labels (local shrinking of rank), and a maximally separated structure (high-rank structure) on the predictions of instances from different labels (global expanding of rank). In this way, these imposed low-rank structures can help modeling both local and global low-rank label structures, while the imposed high-rank structure can help providing more underlying discriminability. Our subsequent theoretical analysis also supports these intuitions. In addition, we provide a nonlinear extension via using kernel trick to enhance DM2L and establish a concave-convex objective to learn these models. Compared to the other methods, our method involves the fewest assumptions and only one hyper-parameter. Even so, extensive experiments show that our method still outperforms the state-of-the-art methods.
Chest radiography is one of the most common types of diagnostic radiology exams, which is critical for screening and diagnosis of many different thoracic diseases. Specialized algorithms have been ...developed to detect several specific pathologies such as lung nodules or lung cancer. However, accurately detecting the presence of multiple diseases from chest X-rays (CXRs) is still a challenging task. This paper presents a supervised multi-label classification framework based on deep convolutional neural networks (CNNs) for predicting the presence of 14 common thoracic diseases and observations. We tackle this problem by training state-of-the-art CNNs that exploit hierarchical dependencies among abnormality labels. We also propose to use the label smoothing technique for a better handling of uncertain samples, which occupy a significant portion of almost every CXR dataset. Our model is trained on over 200,000 CXRs of the recently released CheXpert dataset and achieves a mean area under the curve (AUC) of 0.940 in predicting 5 selected pathologies from the validation set. This is the highest AUC score yet reported to date. The proposed method is also evaluated on the independent test set of the CheXpert competition, which is composed of 500 CXR studies annotated by a panel of 5 experienced radiologists. The performance is on average better than 2.6 out of 3 other individual radiologists with a mean AUC of 0.930, which ranks first on the CheXpert leaderboard at the time of writing this paper.
Delving Deep Into Label Smoothing Zhang, Chang-Bin; Jiang, Peng-Tao; Hou, Qibin ...
IEEE transactions on image processing,
2021, Letnik:
30
Journal Article
Recenzirano
Odprti dostop
Label smoothing is an effective regularization tool for deep neural networks (DNNs), which generates soft labels by applying a weighted average between the uniform distribution and the hard label. It ...is often used to reduce the overfitting problem of training DNNs and further improve classification performance. In this paper, we aim to investigate how to generate more reliable soft labels. We present an Online Label Smoothing (OLS) strategy, which generates soft labels based on the statistics of the model prediction for the target category. The proposed OLS constructs a more reasonable probability distribution between the target categories and non-target categories to supervise DNNs. Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets. Additionally, the proposed method can significantly improve the robustness of DNN models to noisy labels compared to current label smoothing approaches. The source code is available at our project page: https://mmcheng.net/ols/
Continuous label distribution learning Zhao, Xingyu; An, Yuexuan; Xu, Ning ...
Pattern recognition,
January 2023, 2023-01-00, Letnik:
133
Journal Article
Recenzirano
•We propose a novel LDL method named CLDL which can utilize the continuous label distribution information to conduct models.•We describe labels as a continuous distribution in the latent space, where ...only a few parameters require to be learned.•We propose an effective and scalable strategy for learning continuous label distribution based on theoretical analysis.•We systematically analyze the CLDL method. The analysis illustrates the superiority of CLDL to the existing LDL algorithms.
Label distribution learning (LDL) is a suitable paradigm to deal with label ambiguity through learning the correlations among different labels. Most existing label distribution learning methods consider the labels to be discrete and directly establish the mapping from features to labels. However, in many real-world applications, labels naturally form a continuous distribution, which is ignored by the existing methods. As a result, the distribution information of labels can not be accurately described and finally affects the whole learning system. The goal of this paper is to propose a novel approach which can capture the continuous distribution of different labels explicitly and effectively. Specifically, we propose Continuous Label Distribution Learning (CLDL) which describes labels as a continuous density function and learns the distribution information of the labels in the latent space. In this way, the high-order correlations among different labels can be effectively extracted and only a few parameters for describing the continuous distribution need to be learned. Extensive description degree prediction experiments on real-world datasets validate the superiority of CLDL over the existing approaches.
The passage of the Best Pharmaceuticals for Children Act and the Pediatric Research Equity Act has collectively resulted in an improvement in rational prescribing for children, including more than ...500 labeling changes. However, off-label drug use remains an important public health issue for infants, children, and adolescents, because an overwhelming number of drugs still have no information in the labeling for use in pediatrics. The purpose of off-label use is to benefit the individual patient. Practitioners use their professional judgment to determine these uses. As such, the term "off-label" does not imply an improper, illegal, contraindicated, or investigational use. Therapeutic decision-making must always rely on the best available evidence and the importance of the benefit for the individual patient.
Label missing and class imbalance problems are two hot research topics in machine learning, and they have been impeding the improvement of model performance, especially in the multi-label learning. ...Although some existing methods have proven to be effective, they are suitable for only one case. How to effectively address above two issues simultaneously is a challenging problem. In this paper, we propose a novel model named Imbalanced and Missing multi-Label data learning with Global and Local structure (IMLGL) to address the aforementioned challenge. There are following three advantages. At the empirical risk level, we introduce the label correlation matrix C into the loss function and devise a dynamic weighting method to address the aforementioned challenge. At the data level, we analyze the structural characteristics of the data, and introduce local low-rank and global high-rank term to enhance the generalization performance of the model. At the label level, a smoothing term is also introduced for learning the constraint classifier coefficient matrix W. Our method utilizes alternative optimization technique and alternating minimization method for solving. Extensive experiments on six datasets demonstrate the competitiveness of our approach.
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