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Статті в журналах з теми "Label selection"

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Автор: Grafiati

Опубліковано: 28 вересня 2022

Оновлено: 28 січня 2023

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1

Wu, Xingyu, Bingbing Jiang, Kui Yu, Huanhuan Chen, and Chunyan Miao. "Multi-Label Causal Feature Selection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6430–37. http://dx.doi.org/10.1609/aaai.v34i04.6114.

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Multi-label feature selection has received considerable attentions during the past decade. However, existing algorithms do not attempt to uncover the underlying causal mechanism, and individually solve different types of variable relationships, ignoring the mutual effects between them. Furthermore, these algorithms lack of interpretability, which can only select features for all labels, but cannot explain the correlation between a selected feature and a certain label. To address these problems, in this paper, we theoretically study the causal relationships in multi-label data, and propose a novel Markov blanket based multi-label causal feature selection (MB-MCF) algorithm. MB-MCF mines the causal mechanism of labels and features first, to obtain a complete representation of information about labels. Based on the causal relationships, MB-MCF then selects predictive features and simultaneously distinguishes common features shared by multiple labels and label-specific features owned by single labels. Experiments on real-world data sets validate that MB-MCF could automatically determine the number of selected features and simultaneously achieve the best performance compared with state-of-the-art methods. An experiment in Emotions data set further demonstrates the interpretability of MB-MCF.

2

Zhang, Ping, Wanfu Gao, Juncheng Hu, and Yonghao Li. "Multi-Label Feature Selection Based on High-Order Label Correlation Assumption." Entropy 22, no. 7 (July 21, 2020): 797. http://dx.doi.org/10.3390/e22070797.

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Multi-label data often involve features with high dimensionality and complicated label correlations, resulting in a great challenge for multi-label learning. Feature selection plays an important role in multi-label learning to address multi-label data. Exploring label correlations is crucial for multi-label feature selection. Previous information-theoretical-based methods employ the strategy of cumulative summation approximation to evaluate candidate features, which merely considers low-order label correlations. In fact, there exist high-order label correlations in label set, labels naturally cluster into several groups, similar labels intend to cluster into the same group, different labels belong to different groups. However, the strategy of cumulative summation approximation tends to select the features related to the groups containing more labels while ignoring the classification information of groups containing less labels. Therefore, many features related to similar labels are selected, which leads to poor classification performance. To this end, Max-Correlation term considering high-order label correlations is proposed. Additionally, we combine the Max-Correlation term with feature redundancy term to ensure that selected features are relevant to different label groups. Finally, a new method named Multi-label Feature Selection considering Max-Correlation (MCMFS) is proposed. Experimental results demonstrate the classification superiority of MCMFS in comparison to eight state-of-the-art multi-label feature selection methods.

3

Robitaille, Nicolas, and Simon Duchesne. "Label Fusion Strategy Selection." International Journal of Biomedical Imaging 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/431095.

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Label fusion is used in medical image segmentation to combine several different labels of the same entity into a single discrete label, potentially more accurate, with respect to the exact, sought segmentation, than the best input element. Using simulated data, we compared three existing label fusion techniques—STAPLE, Voting, and Shape-Based Averaging (SBA)—and observed that none could be considered superior depending on the dissimilarity between the input elements. We thus developed an empirical, hybrid technique called SVS, which selects the most appropriate technique to apply based on this dissimilarity. We evaluated the label fusion strategies on two- and three-dimensional simulated data and showed that SVS is superior to any of the three existing methods examined. On real data, we used SVS to perform fusions of 10 segmentations of the hippocampus and amygdala in 78 subjects from the ICBM dataset. SVS selected SBA in almost all cases, which was the most appropriate method overall.

4

Wang, Xiujuan, and Yuchen Zhou. "Multi-Label Feature Selection with Conditional Mutual Information." Computational Intelligence and Neuroscience 2022 (October 8, 2022): 1–13. http://dx.doi.org/10.1155/2022/9243893.

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Feature selection is an important way to optimize the efficiency and accuracy of classifiers. However, traditional feature selection methods cannot work with many kinds of data in the real world, such as multi-label data. To overcome this challenge, multi-label feature selection is developed. Multi-label feature selection plays an irreplaceable role in pattern recognition and data mining. This process can improve the efficiency and accuracy of multi-label classification. However, traditional multi-label feature selection based on mutual information does not fully consider the effect of redundancy among labels. The deficiency may lead to repeated computing of mutual information and leave room to enhance the accuracy of multi-label feature selection. To deal with this challenge, this paper proposed a multi-label feature selection based on conditional mutual information among labels (CRMIL). Firstly, we analyze how to reduce the redundancy among features based on existing papers. Secondly, we propose a new approach to diminish the redundancy among labels. This method takes label sets as conditions to calculate the relevance between features and labels. This approach can weaken the impact of the redundancy among labels on feature selection results. Finally, we analyze this algorithm and balance the effects of relevance and redundancy on the evaluation function. For testing CRMIL, we compare it with the other eight multi-label feature selection algorithms on ten datasets and use four evaluation criteria to examine the results. Experimental results illustrate that CRMIL performs better than other existing algorithms.

5

Zhu, Pengfei, Qian Xu, Qinghua Hu, Changqing Zhang, and Hong Zhao. "Multi-label feature selection with missing labels." Pattern Recognition 74 (February 2018): 488–502. http://dx.doi.org/10.1016/j.patcog.2017.09.036.

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6

Lin, Yaojin, Qinghua Hu, Jia Zhang, and Xindong Wu. "Multi-label feature selection with streaming labels." Information Sciences 372 (December 2016): 256–75. http://dx.doi.org/10.1016/j.ins.2016.08.039.

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7

Lee, Jaesung, and Dae-Won Kim. "Efficient Multi-Label Feature Selection Using Entropy-Based Label Selection." Entropy 18, no. 11 (November 15, 2016): 405. http://dx.doi.org/10.3390/e18110405.

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8

Xu, Yuanyuan, Jun Wang, and Jinmao Wei. "To Avoid the Pitfall of Missing Labels in Feature Selection: A Generative Model Gives the Answer." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 6534–41. http://dx.doi.org/10.1609/aaai.v34i04.6127.

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In multi-label learning, instances have a large number of noisy and irrelevant features, and each instance is associated with a set of class labels wherein label information is generally incomplete. These missing labels possess two sides like a coin; people cannot predict whether their provided information for feature selection is favorable (relevant) or not (irrelevant) during tossing. Existing approaches either superficially consider the missing labels as negative or indiscreetly impute them with some predicted values, which may either overestimate unobserved labels or introduce new noises in selecting discriminative features. To avoid the pitfall of missing labels, a novel unified framework of selecting discriminative features and modeling incomplete label matrix is proposed from a generative point of view in this paper. Concretely, we relax Smoothness Assumption to infer the label observability, which can reveal the positions of unobserved labels, and employ the spike-and-slab prior to perform feature selection by excluding unobserved labels. Using a data-augmentation strategy leads to full local conjugacy in our model, facilitating simple and efficient Expectation Maximization (EM) algorithm for inference. Quantitative and qualitative experimental results demonstrate the superiority of the proposed approach under various evaluation metrics.

9

Zhang, Ping, Guixia Liu, Wanfu Gao, and Jiazhi Song. "Multi-label feature selection considering label supplementation." Pattern Recognition 120 (December 2021): 108137. http://dx.doi.org/10.1016/j.patcog.2021.108137.

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10

Ma, Jianghong, and Tommy W. S. Chow. "Label-specific feature selection and two-level label recovery for multi-label classification with missing labels." Neural Networks 118 (October 2019): 110–26. http://dx.doi.org/10.1016/j.neunet.2019.04.011.

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11

Paul, Dipanjyoti, Rahul Kumar, Sriparna Saha, and Jimson Mathew. "Multi-objective Cuckoo Search-based Streaming Feature Selection for Multi-label Dataset." ACM Transactions on Knowledge Discovery from Data 15, no. 6 (May 19, 2021): 1–24. http://dx.doi.org/10.1145/3447586.

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The feature selection method is the process of selecting only relevant features by removing irrelevant or redundant features amongst the large number of features that are used to represent data. Nowadays, many application domains especially social media networks, generate new features continuously at different time stamps. In such a scenario, when the features are arriving in an online fashion, to cope up with the continuous arrival of features, the selection task must also have to be a continuous process. Therefore, the streaming feature selection based approach has to be incorporated, i.e., every time a new feature or a group of features arrives, the feature selection process has to be invoked. Again, in recent years, there are many application domains that generate data where samples may belong to more than one classes called multi-label dataset. The multiple labels that the instances are being associated with, may have some dependencies amongst themselves. Finding the co-relation amongst the class labels helps to select the discriminative features across multiple labels. In this article, we develop streaming feature selection methods for multi-label data where the multiple labels are reduced to a lower-dimensional space. The similar labels are grouped together before performing the selection method to improve the selection quality and to make the model time efficient. The multi-objective version of the cuckoo search-based approach is used to select the optimal feature set. The proposed method develops two versions of the streaming feature selection method: ) when the features arrive individually and ) when the features arrive in the form of a batch. Various multi-label datasets from various domains such as text, biology, and audio have been used to test the developed streaming feature selection methods. The proposed methods are compared with many previous feature selection methods and from the comparison, the superiority of using multiple objectives and label co-relation in the feature selection process can be established.

12

He, Zhi-Fen, Ming Yang, Yang Gao, Hui-Dong Liu, and Yilong Yin. "Joint multi-label classification and label correlations with missing labels and feature selection." Knowledge-Based Systems 163 (January 2019): 145–58. http://dx.doi.org/10.1016/j.knosys.2018.08.018.

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13

Malott, Krista M., Kathryn P. Alessandria, Kirkpatrick Megan, and Carandang Justine. "Ethnic Labeling in Mexican-Origin Youth: A Qualitative Assessment." Professional School Counseling 12, no. 5 (June 2009): 2156759X0901200. http://dx.doi.org/10.1177/2156759x0901200505.

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Outcomes are reported from a qualitative investigation addressing ethnic label selection, meaning, use, and influences upon Mexican-origin youth. Participants selected multiple labels with distinct meanings and influences. Findings indicate a need for school counselors to honor student label selection and to advocate for variable label use by school professionals and in school documentation. School counselors can provide resources and venues to facilitate student exploration of ethnic labels, as one key component of ethnic identity development.

14

Wang, Zhenwu, Tielin Wang, Benting Wan, and Mengjie Han. "Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification." Entropy 22, no. 10 (October 10, 2020): 1143. http://dx.doi.org/10.3390/e22101143.

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Multi-label classification (MLC) is a supervised learning problem where an object is naturally associated with multiple concepts because it can be described from various dimensions. How to exploit the resulting label correlations is the key issue in MLC problems. The classifier chain (CC) is a well-known MLC approach that can learn complex coupling relationships between labels. CC suffers from two obvious drawbacks: (1) label ordering is decided at random although it usually has a strong effect on predictive performance; (2) all the labels are inserted into the chain, although some of them may carry irrelevant information that discriminates against the others. In this work, we propose a partial classifier chain method with feature selection (PCC-FS) that exploits the label correlation between label and feature spaces and thus solves the two previously mentioned problems simultaneously. In the PCC-FS algorithm, feature selection is performed by learning the covariance between feature set and label set, thus eliminating the irrelevant features that can diminish classification performance. Couplings in the label set are extracted, and the coupled labels of each label are inserted simultaneously into the chain structure to execute the training and prediction activities. The experimental results from five metrics demonstrate that, in comparison to eight state-of-the-art MLC algorithms, the proposed method is a significant improvement on existing multi-label classification.

15

Wang, Chenxi, Yaojin Lin, and Jinghua Liu. "Feature selection for multi-label learning with missing labels." Applied Intelligence 49, no. 8 (February 23, 2019): 3027–42. http://dx.doi.org/10.1007/s10489-019-01431-6.

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16

WANG, Yibin, Chen WU, Yusheng CHENG, and Jiansheng JIANG. "Multi-label feature selection algorithm with imbalance label otherness." Journal of Shenzhen University Science and Engineering 37, no. 3 (May 1, 2020): 234–42. http://dx.doi.org/10.3724/sp.j.1249.2020.03234.

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17

Liu, Jinghua, Yuwen Li, Wei Weng, Jia Zhang, Baihua Chen, and Shunxiang Wu. "Feature selection for multi-label learning with streaming label." Neurocomputing 387 (April 2020): 268–78. http://dx.doi.org/10.1016/j.neucom.2020.01.005.

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18

Wang, Fei, Lei Zhu, Jingjing Li, Haibao Chen, and Huaxiang Zhang. "Unsupervised soft-label feature selection." Knowledge-Based Systems 219 (May 2021): 106847. http://dx.doi.org/10.1016/j.knosys.2021.106847.

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19

Yan, Pingkun, Yihui Cao, Yuan Yuan, Baris Turkbey, and Peter L. Choyke. "Label Image Constrained Multiatlas Selection." IEEE Transactions on Cybernetics 45, no. 6 (June 2015): 1158–68. http://dx.doi.org/10.1109/tcyb.2014.2346394.

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20

Zhang, Ping, Guixia Liu, and Wanfu Gao. "Distinguishing two types of labels for multi-label feature selection." Pattern Recognition 95 (November 2019): 72–82. http://dx.doi.org/10.1016/j.patcog.2019.06.004.

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21

Schnapp, Shachar, and Sivan Sabato. "Active Feature Selection for the Mutual Information Criterion." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9497–504. http://dx.doi.org/10.1609/aaai.v35i11.17144.

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We study active feature selection, a novel feature selection setting in which unlabeled data is available, but the budget for labels is limited, and the examples to label can be actively selected by the algorithm. We focus on feature selection using the classical mutual information criterion, which selects the k features with the largest mutual information with the label. In the active feature selection setting, the goal is to use significantly fewer labels than the data set size and still find k features whose mutual information with the label based on the entire data set is large. We explain and experimentally study the choices that we make in the algorithm, and show that they lead to a successful algorithm, compared to other more naive approaches. Our design draws on insights which relate the problem of active feature selection to the study of pure-exploration multi-armed bandits settings. While we focus here on mutual information, our general methodology can be adapted to other feature-quality measures as well. The extended version of this paper, reporting all experiment results, is available at Schnapp and Sabato (2020). The code is available at the following url: https://github.com/ShacharSchnapp/ActiveFeatureSelection

22

González, Manuel, José-Ramón Cano, and Salvador García. "ProLSFEO-LDL: Prototype Selection and Label- Specific Feature Evolutionary Optimization for Label Distribution Learning." Applied Sciences 10, no. 9 (April 29, 2020): 3089. http://dx.doi.org/10.3390/app10093089.

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Label Distribution Learning (LDL) is a general learning framework that assigns an instance to a distribution over a set of labels rather than to a single label or multiple labels. Current LDL methods have proven their effectiveness in many real-life machine learning applications. In LDL problems, instance-based algorithms and particularly the adapted version of the k-nearest neighbors method for LDL (AA-kNN) has proven to be very competitive, achieving acceptable results and allowing an explainable model. However, it suffers from several handicaps: it needs large storage requirements, it is not efficient predicting and presents a low tolerance to noise. The purpose of this paper is to mitigate these effects by adding a data reduction stage. The technique devised, called Prototype selection and Label-Specific Feature Evolutionary Optimization for LDL (ProLSFEO-LDL), is a novel method to simultaneously address the prototype selection and the label-specific feature selection pre-processing techniques. Both techniques pose a complex optimization problem with a huge search space. Therefore, we have proposed a search method based on evolutionary algorithms that allows us to obtain a solution to both problems in a reasonable time. The effectiveness of the proposed ProLSFEO-LDL method is verified on several real-world LDL datasets, showing significant improvements in comparison with using raw datasets.

23

Goodman, Samantha, David Hammond, Rhona Hanning, and Judy Sheeshka. "The impact of adding front-of-package sodium content labels to grocery products: an experimental study." Public Health Nutrition 16, no. 3 (August 3, 2012): 383–91. http://dx.doi.org/10.1017/s1368980012003485.

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AbstractObjectiveCanadians consume approximately twice the daily Adequate Intake of sodium. The present study examined the efficacy of four types of front-of-package (FOP) sodium labels at influencing consumers’ selection of products low v. high in sodium.DesignParticipants were randomly assigned to one of five experimental conditions: (i) control condition with no FOP label; (ii) basic numeric FOP label; (iii) numeric FOP label with ‘high’ and ‘low’ sodium content descriptors; (iv) detailed Traffic Light (TL) label with colour coding, content descriptors and numeric information; and (v) simple TL label with no numeric information. Participants were shown pairs of grocery products that varied in sodium content and told they could choose a free sample. Selection of the low-sodium v. the high-sodium product was the primary behavioural outcome, in addition to ratings of effectiveness, understanding, liking and believability.SettingWaterloo, Ontario, Canada.SubjectsAdults (n 430) aged ≥18 years, recruited from community settings.ResultsParticipants in the three FOP conditions with ‘high/low’ sodium content descriptors were significantly more likely to choose the lower-sodium product compared with the control group. The detailed TL label was ranked most effective at helping participants select low-sodium products, and was rated significantly higher than other formats in liking, understanding and believability. Product selection did not differ significantly across sociodemographic groups.ConclusionsFOP labels that include content descriptors may be more effective in helping consumers to select lower-sodium products. TL labels, which incorporate content descriptors and colour coding, should be considered for future FOP labelling initiatives.

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Miller-Spoto, Marcia, and Sara P. Gombatto. "Diagnostic Labels Assigned to Patients With Orthopedic Conditions and the Influence of the Label on Selection of Interventions: A Qualitative Study of Orthopaedic Clinical Specialists." Physical Therapy 94, no. 6 (June 1, 2014): 776–91. http://dx.doi.org/10.2522/ptj.20130244.

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Background A variety of diagnostic classification systems are used by physical therapists, but little information about how therapists assign diagnostic labels and how the labels are used to direct intervention is available. Objective The purposes of this study were: (1) to examine the diagnostic labels assigned to patient problems by physical therapists who are board-certified Orthopaedic Clinical Specialists (OCSs) and (2) to determine whether the label influences selection of interventions. Design A cross-sectional survey was conducted. Methods Two written cases were developed for patients with low back and shoulder pain. A survey was used to evaluate the diagnostic label assigned and the interventions considered important for each case. The cases and survey were sent to therapists who are board-certified OCSs. Respondents assigned a diagnostic label and rated the importance of intervention categories for each case. Each diagnostic label was coded based on the construct it represented. Percentage responses for each diagnostic label code and intervention category were calculated. Relative importance of intervention category based on diagnostic label was examined. Results For the low back pain and shoulder pain cases, respectively, “Combination” (48.5%, 34.9%) and “Pathology/Pathophysiology” (32.7%, 57.3%) diagnostic labels were most common. Strengthening (85.9%, 98.1%), stretching (86.8%, 84.9%), neuromuscular re-education (87.6%, 93.4%), functional training (91.4%, 88.6%), and mobilization/manipulation (85.1%, 86.8%) were considered the most important interventions. Relative importance of interventions did not differ based on diagnostic label (χ2=0.050–1.263, P=.261–.824). Limitations The low response rate may limit the generalizability of the findings. Also, examples provided for labels may have influenced responses, and some of the label codes may have represented overlapping constructs. Conclusions There is little consistency with which OCS therapists assign diagnostic labels, and the label does not seem to influence selection of interventions.

25

Weng, Wei, Yan-Nan Chen, Chin-Ling Chen, Shun-Xiang Wu, and Jing-Hua Liu. "Non-sparse label specific features selection for multi-label classification." Neurocomputing 377 (February 2020): 85–94. http://dx.doi.org/10.1016/j.neucom.2019.10.016.

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26

Wang, Jun, Yuanyuan Xu, Hengpeng Xu, Zhe Sun, Zhenglu Yang, and Jinmao Wei. "An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features." Applied Sciences 10, no. 22 (November 15, 2020): 8093. http://dx.doi.org/10.3390/app10228093.

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Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.

27

Liu, Jinghua, Yaojin Lin, Shunxiang Wu, and Chenxi Wang. "Online Multi-label Group Feature Selection." Knowledge-Based Systems 143 (March 2018): 42–57. http://dx.doi.org/10.1016/j.knosys.2017.12.008.

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Cai, Yaping, Ming Yang, Yang Gao, and Hujun Yin. "ReliefF-based Multi-label Feature Selection." International Journal of Database Theory and Application 8, no. 4 (August 30, 2015): 307–18. http://dx.doi.org/10.14257/ijdta.2015.8.4.31.

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El-Hasnony, Ibrahim M., Omar M. Elzeki, Ali Alshehri, and Hanaa Salem. "Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction." Sensors 22, no. 3 (February 4, 2022): 1184. http://dx.doi.org/10.3390/s22031184.

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The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user–expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.

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An, Wannian, Peichang Zhang, Jiajun Xu, Huancong Luo, Lei Huang, and Shida Zhong. "A Novel Machine Learning Aided Antenna Selection Scheme for MIMO Internet of Things." Sensors 20, no. 8 (April 16, 2020): 2250. http://dx.doi.org/10.3390/s20082250.

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In this article, we propose a multi-label convolution neural network (MLCNN)-aided transmit antenna selection (AS) scheme for end-to-end multiple-input multiple-output (MIMO) Internet of Things (IoT) communication systems in correlated channel conditions. In contrast to the conventional single-label multi-class classification ML schemes, we opt for using the concept of multi-label in the proposed MLCNN-aided transmit AS MIMO IoT system, which may greatly reduce the length of training labels in the case of multi-antenna selection. Additionally, applying multi-label concept may significantly improve the prediction accuracy of the trained MLCNN model under correlated large-scale MIMO channel conditions with less training data. The corresponding simulation results verified that the proposed MLCNN-aided AS scheme may be capable of achieving near-optimal capacity performance in real time, and the performance is relatively insensitive to the effects of imperfect CSI.

31

Cameron, Alan C., James Bogie, Azmil H. Abdul-Rahim, Niaz Ahmed, Michael Mazya, Robert Mikulik, Werner Hacke, and Kennedy R. Lees. "Professional guideline versus product label selection for treatment with IV thrombolysis: An analysis from SITS registry." European Stroke Journal 3, no. 1 (December 8, 2017): 39–46. http://dx.doi.org/10.1177/2396987317747737.

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Introduction Thrombolysis usage in ischaemic stroke varies across sites. Divergent advice from professional guidelines and product labels may contribute. Patients and methods We analysed SITS-International registry patients enrolled January 2010 through June 2016. We grouped sites into organisational tertiles by number of patients arriving ≤2.5 h and treated ≤3 h, percentage arriving ≤2.5 h and treated ≤3 h, and numbers treated ≤3 h. We assigned scores of 1–3 (lower/middle/upper) per variable and 2 for onsite thrombectomy. We classified sites as lower efficiency (summed scores 3–5), medium efficiency (6–8) or higher efficiency (9–11). Sites were also grouped by adherence with European product label and ESO guideline: ‘label adherent’ (>95% on-label), ‘guideline adherent’ (≥5% off-label, ≥95% on-guideline) or ‘guideline non-adherent’ (>5% off-guideline). We cross-tabulated site-efficiency and adherence. We estimated the potential benefit of universally selecting by ESO guidance, using onset-to-treatment time-specific numbers needed to treat for day 90 mRS 0–1. Results A total of 56,689 patients at 597 sites were included: 163 sites were higher efficiency, 204 medium efficiency and 230 lower efficiency. Fifty-six sites were ‘label adherent’, 204 ‘guideline adherent’ and 337 ‘guideline non-adherent’. There were strong associations between site-efficiency and adherence (P < 0.001). Almost all ‘label adherent’ sites (55, 98%) were lower efficiency. If all patients were treated by ESO guidelines, an additional 17,031 would receive alteplase, which translates into 1922 more patients with favourable three-month outcomes. Discussion Adherence with product labels is highest in lower efficiency sites. Closer alignment with professional guidelines would increase patients treated and favourable outcomes. Conclusion Product labels should be revised to allow treatment of patients ≤4.5 h from onset and aged ≥80 years.

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Fan, Yuling, Jinghua Liu, Wei Weng, Baihua Chen, Yannan Chen, and Shunxiang Wu. "Multi-label feature selection with local discriminant model and label correlations." Neurocomputing 442 (June 2021): 98–115. http://dx.doi.org/10.1016/j.neucom.2021.02.005.

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Tan, Anhui, Jiye Liang, Wei-Zhi Wu, Jia Zhang, Lin Sun, and Chao Chen. "Fuzzy rough discrimination and label weighting for multi-label feature selection." Neurocomputing 465 (November 2021): 128–40. http://dx.doi.org/10.1016/j.neucom.2021.09.007.

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Fan, Yuling, Baihua Chen, Weiqin Huang, Jinghua Liu, Wei Weng, and Weiyao Lan. "Multi-label feature selection based on label correlations and feature redundancy." Knowledge-Based Systems 241 (April 2022): 108256. http://dx.doi.org/10.1016/j.knosys.2022.108256.

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Qian, Wenbin, Jintao Huang, Yinglong Wang, and Wenhao Shu. "Mutual information-based label distribution feature selection for multi-label learning." Knowledge-Based Systems 195 (May 2020): 105684. http://dx.doi.org/10.1016/j.knosys.2020.105684.

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Qian, Wenbin, Xuandong Long, Yinglong Wang, and Yonghong Xie. "Multi-label feature selection based on label distribution and feature complementarity." Applied Soft Computing 90 (May 2020): 106167. http://dx.doi.org/10.1016/j.asoc.2020.106167.

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Zhang, Ping, Wanfu Gao, Juncheng Hu, and Yonghao Li. "Multi-label feature selection based on the division of label topics." Information Sciences 553 (April 2021): 129–53. http://dx.doi.org/10.1016/j.ins.2020.12.036.

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Qian, Wenbin, Jintao Huang, Yinglong Wang, and Yonghong Xie. "Label distribution feature selection for multi-label classification with rough set." International Journal of Approximate Reasoning 128 (January 2021): 32–55. http://dx.doi.org/10.1016/j.ijar.2020.10.002.

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Chen, Yan-Nan, Wei Weng, Shun-Xiang Wu, Bai-Hua Chen, Yu-Ling Fan, and Jing-Hua Liu. "An efficient stacking model with label selection for multi-label classification." Applied Intelligence 51, no. 1 (August 12, 2020): 308–25. http://dx.doi.org/10.1007/s10489-020-01807-z.

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Pan, Minlan, Zhanquan Sun, Chaoli Wang, and Gaoyu Cao. "A multi-label feature selection method based on an approximation of interaction information." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 823–40. http://dx.doi.org/10.3233/ida-215985.

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High-dimensional multi-label data is widespread in practical applications, which brings great challenges to the research field of pattern recognition and machine learning. Many feature selection algorithms have been proposed in recent years, among which the filtering feature selection algorithm is the most popular one because of its simplicity. Therefore, filtering feature selection has become a hot research topic, especially the multi-label feature selection algorithm based on mutual information. In the algorithm, the computation cost of high dimensional mutual information is expensive. How to approximate high order mutual information based on low order mutual information has become a major research direction. To our best knowledge, all existing feature selection algorithms that consider the label correlation will increase the computational cost greatly. Therefore, this paper proposes an approximation method of three-dimensional interaction information, which is applied to the calculation of correlation and redundancy. It can take the correlation of labels into account and don’t increase the computation cost significantly at the same time. Experiments analysis results show that the proposed method is effective.

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Yang, Jie, Jinfeng Li, Kun Lan, Anruo Wei, Han Wang, Shigao Huang, and Simon Fong. "Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning." Bioengineering 9, no. 7 (June 22, 2022): 268. http://dx.doi.org/10.3390/bioengineering9070268.

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There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the corresponding labels. Traditional ECG processing approaches rely heavily on prior knowledge, such as those from feature extraction and waveform analysis. The preprocessing for prior knowledge incurs computational overhead. Furthermore, standard deep learning methods do not fully consider the dynamic temporal, spatial and multi-labeling characteristics of ECG data. In clinical ECG waveforms, it is common to see multi-labeling in which a patient is labeled with multiple classes of arrhythmias. However, multiclass approaches in current research mainly solve the multi-label machine learning problem, ignoring the correlation between diseases, resulting in information loss. In this paper, an arrhythmia detection and classification scheme called multi-label fusion deep learning is proposed. The objective is to build a unified system with automatic feature learning which supports effective multi-label classification. First, a multi-label ECG-based feature selection method is combined with a matrix decomposition and sparse learning theory. The optimal feature subset is selected as a preprocessing algorithm for ECG data. A multi-label classifier is then constructed by fusing CNN and RNN networks to fully exploit the interactions and features of the time and space dimensions. The experimental result demonstrates that the proposed method can achieve a state-of-the-art performance compared to other algorithms in multi-label database experiments.

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Eriksson, Kimmo, and Pontus Strimling. "Spontaneous associations and label framing have similar effects in the public goods game." Judgment and Decision Making 9, no. 5 (September 2014): 360–72. http://dx.doi.org/10.1017/s1930297500006756.

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AbstractIt is known that presentation of a meaningful label (e.g., "The Teamwork Game") can influence decisions in economic games. A common view is that such labels cue associations to preexisting mental models of situations, a process here called frame selection. In the absence of such cues, participants may still spontaneously associate a game with a preexisting frame. We used the public goods game to compare the effect of such spontaneous frame selection with the effect of label framing. Participants in a condition where the public goods game was labeled "The Teamwork Game" tended to contribute at the same level as participants who spontaneously associated the unlabeled game with teamwork, whereas those who did not associate the the unlabeled game with teamwork tended to make lower contributions. We conclude that neutrally described games may be subject to spontaneous frame selection effects comparable in size to the effects of label framing.

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Zhang, Yaojie, Huahu Xu, Junsheng Xiao, and Minjie Bian. "JoSDW: Combating Noisy Labels by Dynamic Weight." Future Internet 14, no. 2 (February 2, 2022): 50. http://dx.doi.org/10.3390/fi14020050.

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The real world is full of noisy labels that lead neural networks to perform poorly because deep neural networks (DNNs) are prone to overfitting label noise. Noise label training is a challenging problem relating to weakly supervised learning. The most advanced existing methods mainly adopt a small loss sample selection strategy, such as selecting the small loss part of the sample for network model training. However, the previous literature stopped here, neglecting the performance of the small loss sample selection strategy while training the DNNs, as well as the performance of different stages, and the performance of the collaborative learning of the two networks from disagreement to an agreement, and making a second classification based on this. We train the network using a comparative learning method. Specifically, a small loss sample selection strategy with dynamic weight is designed. This strategy increases the proportion of agreement based on network predictions, gradually reduces the weight of the complex sample, and increases the weight of the pure sample at the same time. A large number of experiments verify the superiority of our method.

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Krešić, Greta, Nikolina Liović, and Jelka Pleadin. "Effects of menu labelling on students′ food choice: a preliminary study." British Food Journal 121, no. 2 (February 4, 2019): 479–91. http://dx.doi.org/10.1108/bfj-03-2018-0188.

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PurposeThe purpose of this paper is to assess the relationship between nutrition knowledge and grocery store nutrition label use, with using nutrition information disclosure on menu selection in a group of hospitality management students, who shall be responsible for menu labelling in their future careers.Design/methodology/approachA between-subject design included 324 students, who were randomly assigned to choose from a menu labelled as follows: unlabelled; kcal label only; graphical label providing information on the per cent of the recommended daily intake of energy and four nutrients. Their nutrition knowledge and habit of reading grocery store nutrition labels were tested using an additional questionnaire.FindingsThe results showed that the provision of energy value information resulted in the selection of less energetic, less fat and less salted food, while a graphical label additionally led to the selection of food having a lower saturated fatty acid (SFA) and sugar content. Multiple regression analysis showed that the habit of packaged food nutrition label reading was a significant predictor of choosing food having a lower energy (p<0.001), fat (p<0.001), SFA (p<0.001), sugar (p<0.001) and salt (p=0.003) content, while the influence of nutrition knowledge on food selection was proven insignificant.Originality/valueGiven the established positive impact of menu labelling, these findings support the future European policy mandating energy and nutrient content disclosure on menus, but also point to the need for more-intense consumer education.

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Qian, Wenbin, Yinsong Xiong, Jun Yang, and Wenhao Shu. "Feature selection for label distribution learning via feature similarity and label correlation." Information Sciences 582 (January 2022): 38–59. http://dx.doi.org/10.1016/j.ins.2021.08.076.

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Pereira, Rafael B., Alexandre Plastino, Bianca Zadrozny, and Luiz H. C. Merschmann. "A lazy feature selection method for multi-label classification." Intelligent Data Analysis 25, no. 1 (January 26, 2021): 21–34. http://dx.doi.org/10.3233/ida-194878.

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In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

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FUJIYOSHI, Akio, and Masakazu SUZUKI. "Minimum Spanning Tree Problem with Label Selection." IEICE Transactions on Information and Systems E94-D, no. 2 (2011): 233–39. http://dx.doi.org/10.1587/transinf.e94.d.233.

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Arnaiz-González, Álvar, José-Francisco Díez-Pastor, Juan J. Rodríguez, and César García-Osorio. "Local sets for multi-label instance selection." Applied Soft Computing 68 (July 2018): 651–66. http://dx.doi.org/10.1016/j.asoc.2018.04.016.

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Gabathuler, Michael, and Michael Kirschner. "A comparison of workplace-related labels in Switzerland." International Journal of Workplace Health Management 12, no. 6 (November 21, 2019): 405–23. http://dx.doi.org/10.1108/ijwhm-03-2019-0037.

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Purpose In Switzerland, the first and only Swiss quality label for systematic workplace health management (WHM) competes with a variety of national and international workplace-related labels for the attention of employers. The purpose of this paper is to compare the label “Friendly Work Space” (FWS) with ten other national and international workplace-related labels on the “Swiss label market” and to identify key success elements for the development and dissemination of WHM labels. Design/methodology/approach A literature review and qualitative analysis of publicly available documents were conducted. Information was obtained from providers or by the authors’ own research. A description of workplace-related labels is presented based on defined criteria and a typology classifying workplace-related labels available in Switzerland. Findings Workplace-related labels can be differentiated in terms of: deliberate registration vs non-requested selection, policy vs marketing approach and assessment vs survey-based analysis. In terms of sustainable dissemination, FWS is the most successful registration-based label in Switzerland regarding the number of employees and employers benefitting from the label. Therefore, it constitutes a best practice approach for developing and disseminating a WHM label. Originality/value To the authors’ knowledge, this study is the first to systematically analyse and compare a WHM with other workplace-related labels on a national market (supply and demand, quality, dissemination). The authors suggest a specific typology to describe the market. Recommendations are given to build up and successfully disseminate a WHM label on a national scale.

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Zhang, Min-Ling, Jun-Peng Fang, and Yi-Bo Wang. "BiLabel-Specific Features for Multi-Label Classification." ACM Transactions on Knowledge Discovery from Data 16, no. 1 (July 3, 2021): 1–23. http://dx.doi.org/10.1145/3458283.

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In multi-label classification, the task is to induce predictive models which can assign a set of relevant labels for the unseen instance. The strategy of label-specific features has been widely employed in learning from multi-label examples, where the classification model for predicting the relevancy of each class label is induced based on its tailored features rather than the original features. Existing approaches work by generating a group of tailored features for each class label independently, where label correlations are not fully considered in the label-specific features generation process. In this article, we extend existing strategy by proposing a simple yet effective approach based on BiLabel-specific features. Specifically, a group of tailored features is generated for a pair of class labels with heuristic prototype selection and embedding. Thereafter, predictions of classifiers induced by BiLabel-specific features are ensembled to determine the relevancy of each class label for unseen instance. To thoroughly evaluate the BiLabel-specific features strategy, extensive experiments are conducted over a total of 35 benchmark datasets. Comparative studies against state-of-the-art label-specific features techniques clearly validate the superiority of utilizing BiLabel-specific features to yield stronger generalization performance for multi-label classification.