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A survey of multi-label classification based on supervised and semi-supervised learning

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Abstract

Multi-label classification algorithms based on supervised learning use all the labeled data to train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label all the data needed. Multi-label classification algorithms based on semi-supervised learning can use both labeled and unlabeled data to train classifiers, resulting in better-performing models. In this paper, we first review supervised learning classification algorithms in terms of label non-correlation and label correlation and semi-supervised learning classification algorithms in terms of inductive methods and transductive methods. After that, multi-label classification algorithms are introduced from the application areas of image, text, music and video. Subsequently, evaluation metrics and datasets are briefly introduced. Finally, research directions in complex concept drift, label complex correlation, feature selection and class imbalance are presented.

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References

  1. Zhang X, Han M, Wu H et al (2021) An overview of complex data stream ensemble classification. J Intell Fuzzy Syst 1–29

  2. Ma J, Zhang H, Chow TWS (2021) Multilabel classification with label-specific features and classifiers: a coarse- and fine-tuned framework. IEEE Trans Cybern 51(2):1028–1042

    Article  Google Scholar 

  3. Read J, Pfahringer B, Holmes G et al (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333–359

    Article  MathSciNet  Google Scholar 

  4. Tsoumakas G, Vlahavas I (2007) Random k-Labelsets: an ensemble method for multilabel classification. In: Proc of the 18th European conference on machine learning, Springer Berlin Heidelberg. Lecture Notes in Computer Science, Warsaw, pp 406–417

  5. Oliveira E, Ciarelli PM, Badue C et al (2008) A comparison between a KNN based approach and a PNN algorithm for a multi-label classification problem. In: Proc of the eighth international conference on intelligent systems design applications. IEEE, Kaohsiung, pp 628–633

  6. Clare A, King RD (2001) Knowledge discovery in multi-label phenotype data. In: Proc of the European conference on principles of data mining and knowledge discovery. Lecture notes in computer science, Freiburg, pp 42–53

  7. Li X, Wang L, Sung E (2004) Multi-label SVM active learning for image classification. In: Proc of the image processing. IEEE, Singapore, pp 2207–2210

  8. Sapozhnikova EP (2009) Multi-label classification with ART neural networks. In: Proc of the 2009 second international workshop on knowledge discovery and data mining. IEEE, Moscow, pp 144–147

  9. Tsoumakas G, Katakis I (2009) Multi-label classification: an overview. Int J Data Warehouse Min 3(3):1–13

    Google Scholar 

  10. Moyano JM, Gibaja EL, Cios KJ et al (2018) Review of ensembles of multi-label classifiers: models, experimental study and prospects. Inf Fusion 44:33–45

    Article  Google Scholar 

  11. Zheng X, Li P, Chu Z et al (2020) A survey on multi-label data stream classification. IEEE Access 8:1249–1275

    Article  Google Scholar 

  12. Engelen JE, Hoos HH (2020) A survey of semi-supervised learning. Mach Learn 109(2):373–440

    Article  MathSciNet  MATH  Google Scholar 

  13. Li P, Wang H, Bhm C et al (2020) Online semi-supervised multi-label classification with label compression and local smooth regression. In: Proc of the twenty-ninth international joint conference on artificial intelligence, Yokohama, pp 1359–1365

  14. Wang Z, Wang T, Wan B et al (2020) Partial classifier chains with feature selection by exploiting label correlation in multi-label classification. Entropy 22(10):1–22

    Article  MathSciNet  Google Scholar 

  15. Bezembinder EM, Wismans LJJ, Berkum ECV (2017) Constructing multi-labelled decision trees for junction design using the predicted probabilities. In: Proc of the 20th IEEE international conference on intelligent transportation systems. IEEE, Yokohama, pp 1–7

  16. Majzoubi M, Choromanska A (2019) LdSM: logarithm-depth streaming multi-label decision trees. In: Proc of the 23rd international conference on artificial intelligence and statistics, Palermo, pp 4247–4257

  17. Moral-García S, Mantas CJ, Castellano JG et al (2020) Non-parametric predictive inference for solving multi-label classification. Appl Soft Comput 88

  18. Yang Y, Ding M (2019) Decision function with probability feature weighting based on Bayesian network for multi-label classification. Neural Comput Appl 31(9):4819–4828

    Article  Google Scholar 

  19. Sun Z, Hu K, Hu T et al (2018) Fast multi-label low-rank linearized SVM classification algorithm based on approximate extreme points. IEEE Access 42319–42326

  20. Sun Z, Liu X, Hu K et al (2020) An efficient multi-label SVM classification algorithm by combining approximate extreme points method and divide-and-conquer strategy. IEEE Access 8:170967–170975

    Article  Google Scholar 

  21. Wang X, Guo Z, Wang X et al (2019) A fast neural network multi-label classification algorithm based on approximate extreme points. In: 2019 5th international conference on big data computing and communications (BIGCOM)

  22. Wang X, Guo Z, Wang X, et al (2019) NNMLInf: social influence prediction with neural network multi-label classification. In: Proc of the ACM turing celebration conference, vol 106. ACM, Chengdu, pp 1–5

  23. Bello M, Gonzalo N, Ricardo S, et al (2020) Deep neural network to extract high-level features and labels in multi-label classification problems. Neurocomputing 413

  24. Sumbul G, Begüm D (2020) A deep multi-attention driven approach for multi-label remote sensing image classification. IEEE Access 8:95934–95946

    Article  Google Scholar 

  25. Xu X, Shan D, Li S et al (2019) Multi-label learning method based on ML-RBF and laplacian ELM. Neurocomputing

  26. Luo F, Guo W, Yu Y et al (2017) A multi-label classification algorithm based on Kernel extreme learning machine. Neurocomputing 260(Oct 18):313–320

    Article  Google Scholar 

  27. Rezaei M, Eftekhari M, Movahed FS (2020) ML-CK-ELM: an efficient multi-layer extreme learning machine using combined kernels for multi-label classification. Scientia Iranica (6)

  28. Rr A, Mea B, Sm C Regularizing extreme learning machine by dual locally linear embedding manifold learning for training multi-label neural network classifiers. Eng Appl Artif Intell 97

  29. Sadhukhan P, Palit S (2020) Multi-label learning on principles of reverse k-nearest neighbourhood. Expert Syst

  30. Roseberry M, Krawczyk B, Cano A (2019) Multi-label punitive kNN with self-adjusting memory for drifting data streams. ACM Transactions on Knowledge Discovery from Data

  31. Roseberry M, Krawczyk B, Djenouri Y et al (2021) Self-adjusting k nearest neighbors for continual learning from multi-label drifting data streams. Neurocomputing

  32. Wei X, Yu Z, Zhang C et al (2018) Ensemble of label specific features for multi-label classification. In: Proc of the 2018 IEEE international conference on multimedia and expo. IEEE, San Diego, pp 1–6

  33. Alberghini G, Junior SB, Cano A (2022) Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams. Neurocomputing 481:228–248

    Article  Google Scholar 

  34. Lee CH (2018) Multi-label classification of documents using fine-grained weights and modified co-training. Intell Data Analysis 22(1):103–115

    Article  Google Scholar 

  35. Lotf H, Ramdani M (2020) Multi-label classification: a novel approach using decision trees for learning label-relations and preventing cyclical dependencies: Relations Recognition and Removing Cycles (3RC). In: Proc of the 13th international conference on intelligent systems: theories and applications. ACM, Rabat, pp 1–6

  36. Law A, Ghosh A (2021) Multi-label classification using binary tree of classifiers. IEEE Trans Emerg Topics Comput Intell 6(3):677–689

    Article  Google Scholar 

  37. Kim H, Park J, Kim D, Lee J (2020) Multilabel naïve Bayes classification considering label dependence. Pattern Recogn Lett 136:279–285

    Article  Google Scholar 

  38. Jiménez BR, Morales EF, Escalante HJ (2018) Bayesian chain classifier with feature selection for multi-label classification 232–243

  39. Wang R, Ye S, Li K et al (2020) Bayesian network based label correlation analysis for multi-label classifier chain. Inf Sci 554(8)

  40. Wu G, Zheng R, Tian Y, Liu D (2020) Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification. Neural Netw 122:24–39

    Article  MATH  Google Scholar 

  41. Koda S, Zeggada A, Melgani F et al (2018) Spatial and structured SVM for multilabel image classification. IEEE Trans Geosci Remote Sens 56(10):5948–5960

    Google Scholar 

  42. Chen B, Li J, Lu G et al (2019) Label co-occurrence learning with graph convolutional networks for multi-label chest X-ray image classification. IEEE J Biomed Health Inform 24(8):2292–2302

    Article  Google Scholar 

  43. Li J, Fu G, Chen Y et al (2020) A multi-label classification model for full slice brain computerised tomography image. BMC Bioinform. https://doi.org/10.1186/s12859-020-3503-0

    Article  Google Scholar 

  44. He J, Li C, Ye J, Qiao Y, Gu L (2021) Multi-label ocular disease classification with a dense correlation deep neural network. Biomed Signal Process Control 63:102167

    Article  Google Scholar 

  45. Krishna GS, Prakash N (2021) Deep learning for efficient and multi-labelled classification of synthetic aperture radar images. Evolv Syst, 1–14

  46. Zhou C, Chen H, Jing Z, et al (2021) Multi-label graph node classification with label attentive neighborhood convolution. Expert Syst Appl

  47. Nan Z, Ding S, Jian Z (2016) Multi layer ELM-RBF for multi-label learning. Appl Soft Comput 43:535–545

    Article  Google Scholar 

  48. Wang R, Kwong S, Jia Y et al (2021) Active k-labelsets ensemble for multi-label classification. Pattern Recogn 109

  49. Wang R, Kwong S, Jia Y et al (2018) Mutual information based K-labelsets ensemble for multi-label classification. In: Proceedings of the 2018 IEEE international conference on fuzzy systems. IEEE, Rio de Janeiro, pp 1–7

  50. Li GZ, Yang JY, Lu WC et al (2008) Improving prediction accuracy of drug activities by utilising unlabelled instances with feature selection. Int J Comput Biol Drug Des 1(1):1–13

    Article  Google Scholar 

  51. Zhang M-L, Zhou Z-H (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038–2048

    Article  MATH  Google Scholar 

  52. Li GZ, You M, Ge L et al (2010) Feature selection for semi-supervised multi-label learning with application to gene function analysis. In: Proceedings of the first ACM international conference on bioinformatics and computational biology, pp 354–357

  53. Xu M, Sun F, Jiang X (2014) Multi-label learning with co-training based on semi-supervised regression. In: Proceedings of the IEEE international conference on security, pp 175–180

  54. Gharroudi O, Elghazel H, Aussem A (2017) A semi-supervised ensemble approach for multi-label learning. In: Proceedings of the IEEE international conference on data mining workshops. IEEE, pp 1197–1204

  55. Ai Q, Kang Y, Wang A et al (2020) An effective semi-supervised multi-label least squares twin support vector machine. IEEE Access 8:213460–213472

    Article  Google Scholar 

  56. Gharebaghi F, Amiri A (2021) LP-MLTSVM: laplacian multi-label twin support vector machine for semi-supervised classification. IEEE Access 10:13738–13752

    Article  Google Scholar 

  57. Zhan W, Zhang ML (2017) Inductive semi-supervised multi-label learning with co-training. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1305–1314

  58. Settouti N, Douibi K, Bechar MEA et al (2019) Semi-supervised learning with collaborative bagged multi-label K-nearest-neighbors. Open Comput Sci 9(1):226–242

    Article  Google Scholar 

  59. Chu Z, Li P, Hu X (2019) Co-training based on semi-supervised ensemble classification approach for multi-label data stream. In: Proceedings of the 2019 IEEE international conference on big knowledge (ICBK). IEEE, pp 58–65

  60. Alalga A, Benabdeslem K, Taleb N Soft-constrained Laplacian score for semi-supervised multi-label feature selection. Knowledge Inf Syst

  61. Santos AM, Canuto A (2014) Applying the self-training semi-supervised learning in hierarchical multi-label methods. In: Proceedings of the international joint conference on neural networks, pp 872–879

  62. Santos AM, Canuto AMP (2012) Using semi-supervised learning in multi-label classification problems. In: Proceedings of the 2012 international joint conference on neural networks, pp 1–8

  63. Rodrigues FM, Canuto A, Santos AM (2014) Confidence factor and feature selection for semi-supervised multi-label classification methods. In: Proceedings of the international joint conference on neural networks, pp 864–871

  64. Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336

    Article  MATH  Google Scholar 

  65. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  MATH  Google Scholar 

  66. Zhao C, Zhai S (2016) Minimum variance semi-supervised boosting for multi-label classification. In: Proceedings of the IEEE global conference on signal & information processing. IEEE

  67. Ahmed MS, Khan L, Oza NC et al (2010) Multi-label asrs dataset classification using semi-supervised subspace clustering. In: Proceedings of the 2010 conference on intelligent data understanding, pp 285-299

  68. Mehrkanoon S, Suykens J (2016) Multi-label semi-supervised learning using regularized kernel spectral clustering. In: Proceedings of the international joint conference on neural networks. IEEE, pp 4009–4016

  69. Amorim WP, Falcao AX, Papa JP (2018) Multi-label semi-supervised classification through optimum-path forest. Inf Sci 465:86–104

    Article  MathSciNet  MATH  Google Scholar 

  70. Pham TN, Nguyen VQ, Tran VH et al (2017) A semi-supervised multi-label classification framework with feature reduction and enrichment. J Inf Telecommun 1(2):141–154

    Google Scholar 

  71. Ha QT, Pham TN, Nguyen VQ et al (2018) A new text semi-supervised multi-label learning model based on using the label-feature relations. In: Proceedings of the computational collective intelligence—10th international conference, pp 403–413

  72. Huang J, Xu L, Qian K et al (2021) Multi-label learning with missing and completely unobserved labels. Data Min Knowl Disc 35(3):1061–1086

    Article  MathSciNet  MATH  Google Scholar 

  73. Jing L, Yang L, Yu J et al (2015) Semi-supervised low-rank mapping learning for multi-label classification. In: Proceedings of the 2015 IEEE conference on computer vision and pattern recognition. IEEE, pp 1483–1491

  74. Sheng L, Yun F (2017) Robust multi-label semi-supervised classification. In: Proceedings of the 2017 IEEE international conference on big data (big data). IEEE, pp 27–36

  75. Sun L, Feng S, Lyu G, Lang C (2019) Robust semi-supervised multi-label learning by triple low-rank regularization. In: Proceedings of the advances in knowledge discovery and data mining, pp 269–280

  76. Sun L, Lyu G, Feng S et al (2021) Beyond missing: weakly-supervised multi-label learning with incomplete and noisy labels. Appl Intell 51(3):1552–1564

    Article  Google Scholar 

  77. Zha ZJ, Tao M, Wang J et al (2009) Graph-based semi-supervised learning with multi-label. J Vis Commun Image Represent 20(2):97–103

    Article  Google Scholar 

  78. Li T, Yan S, Mei T, Kweon I (2009) Local-driven semi-supervised learning with multi-label. In: Proceedings of the 2009 IEEE international conference on multimedia and expo, pp 1508–1511

  79. Bao BK, Li T, Yan S (2009) Hidden-concept driven image decomposition towards semi-supervised multi-label image annotation. In: Proceedings of the first international conference on internet multimedia computing and service, pp 17–24

  80. Jiang JQ (2012) Predicting protein function by multi-label correlated semi-supervised learning. IEEE/ACM Trans Comput Biol Bioinf 9(4):1059–1069

    Article  MathSciNet  Google Scholar 

  81. Liu Y, Nie F, Gao Q (2018) Nuclear-norm based semi-supervised multiple labels learning. Neurocomputing 275:940–947

    Article  Google Scholar 

  82. Ghosh A, SeKhar CC (2017) Label correlation propagation for semi-supervised multi-label learning. In: Proceedings of the pattern recognition and machine intelligence, pp 52–60

  83. Tan Q, Yu Y, Yu G et al (2017) Semi-supervised multi-label classification using incomplete label information. Neurocomputing 260(18):192–202

    Article  Google Scholar 

  84. Behpour S, Xing W, Ziebart BD (2018) ARC: adversarial robust cuts for semi-supervised and multi-label classification. In: Proceedings of the IEEE/CVF conference on computer vision & pattern recognition workshops. IEEE, pp 2704–2711

  85. Gao K, Zhang J, Zhou C (2019) Semi-supervised graph embedding for multi-label graph node classification. 555–567

  86. Tang C, Liu X, Wang P et al (2019) Adaptive hypergraph embedded semi-supervised multi-label image annotation. IEEE Trans Multimedia 21(11):2837–2849

    Article  Google Scholar 

  87. Wu F, Wang Z, Zhang Z et al (2015) Weakly semi-supervised deep learning for multi-label image annotation. IEEE Trans Big Data 1(3):1–1

    Article  Google Scholar 

  88. Wang X, Feng S, Lang C (2018) Semi-supervised dual low-rank feature mapping for multi-label image annotation. Multimedia Tools Appl

  89. Zhang Q, Zhong G, Dong J (2021) A graph-based semi-supervised multi-label learning method based on label correlation consistency. Cognitive Comput 1–10

  90. Song Z, Meng Z, Zhang Y, King I (2021) Semi-supervised multi-label learning for graph-structured data. In: The 30th ACM international conference on information and knowledge management, pp 1723–1733

  91. Boulbazine S, Cabanes G, Matei B et al (2018) Online semi-supervised growing neural gas for multi-label data classification. In: Proceedings of the 2018 international joint conference on neural networks, pp 1–8

  92. Li D, Dick S (2018) Residential household non-intrusive load monitoring via graph-based multi-label semi-supervised learning. In: IEEE transactions on smart grid, pp 1–1

  93. Hu S, Miao D, Pedrycz W (2022) Multi granularity based label propagation with active learning for semi-supervised classification. Expert Syst Appl 192:116276

    Article  Google Scholar 

  94. Bo W, Tu Z, Tsotsos JK (2014) Dynamic label propagation for semi-supervised multi-class multi-label classification. In: Proceedings of the IEEE international conference on computer vision, vol 68, pp 14–23

  95. Dharmadhikari SC, Ingle M, Kulkarni P (2012) Semi supervised learning based text classification model for multi label paradigm. In: Proceedings of the signal processing and information technology—second international joint conference, pp 178–184

  96. Lucena D, Prudencio R (2015) Semi-supervised multi-label k-nearest neighbors classification algorithms. In: Proceedings of the 2015 Brazilian conference on intelligent systems. IEEE, pp 49–54

  97. Gang C, Song Y, Fei W et al (2008) Semi-supervised multi-label learning by solving a sylvester equation. In: Proceedings of the Siam international conference on data mining, pp 410–419

  98. Cevikalp H, Benligiray B, Gerek ON (2019) Semi-supervised robust deep neural networks for multi-label image classification. Pattern Recogn 100:107164

    Article  Google Scholar 

  99. Guan Q, Huang Y (2018) Multi-label chest X-ray image classification via category-wise residual attention learning. Pattern Recogn Lett 130:259–266

    Article  Google Scholar 

  100. Chougrad H, Zouaki H, Alheyane O (2020) Multi-label transfer learning for the early diagnosis of breast cancer. Neurocomputing 392:168–180

    Article  Google Scholar 

  101. Liu J, Lin Y, Li Y et al (2018) Online multi-label streaming feature selection based on neighborhood rough set. Pattern Recogn 84:273–287

    Article  Google Scholar 

  102. Cai J, Sun W, Guan J et al (2020) Multi-ECGNet for ECG arrythmia multi-label classification. IEEE Access 8:110848–110858

    Article  Google Scholar 

  103. Ou X, Gao L, Quan X et al (2022) BFENet: a two-stream interaction CNN method for multi-label ophthalmic diseases classification with bilateral fundus images. Comput Methods Programs Biomed 219:106739

    Article  Google Scholar 

  104. Xu S, Yang X, Guo J et al (2020) CXNet-m3: a deep quintuplet network for multi-lesion classification in Chest X-ray Images via multi-label supervision. IEEE Access 8:98693–98704

    Article  Google Scholar 

  105. Hua Y, Mou L, Zhu XX (2020) Relation network for multilabel aerial image classification. IEEE Trans Geosci Remote Sens 58:4558–4572

    Article  Google Scholar 

  106. Alshehri A, Bazi Y, Ammour N et al (2019) Deep attention neural network for multi-label classification in unmanned aerial vehicle imagery. IEEE Access 7:119873–119880

    Article  Google Scholar 

  107. Chaudhuri B, Demir B, Chaudhuri S et al (2018) Multilabel remote sensing image retrieval using a semisupervised graph-theoretic method. IEEE Trans Geosci Remote Sens 56(2):1144–1158

    Article  Google Scholar 

  108. Dai OE, Demir B, Sankur B, Bruzzone L (2018) A novel system for content-based retrieval of single and multi-label high-dimensional remote sensing images. IEEE J Sel Top Appl Earth Observ Remote Sensing 11(7):2473–2490

    Article  Google Scholar 

  109. He H, Xia R (2018) Joint binary neural network for multi-label learning with applications to emotion classification. 11108:250–259

  110. Yu J, Luís M, Jiang J et al (2018) Improving multi-label emotion classification via sentiment classification with dual attention transfer network. In: Proceedings of the 2018 conference on empirical methods in natural language processing

  111. Fei H, Ji D, Zhang Y et al (2020) Topic-enhanced capsule network for multi-label emotion classification. IEEE/ACM Trans Audio Speech Lang Process 28:1839–1848

    Article  Google Scholar 

  112. Alzu'Bi S, Badarneh O, Hawashin B et al (2019) Multi-label emotion classification for Arabic tweets. In: Proc of the 2019 sixth international conference on social networks analysis. IEEE, Granada, pp 499–504

  113. Bravo-Marquez F, Frank E, Mohammad SM et al (2016) Determining word-emotion associations from tweets by multi-label classification. In: Proc of the IEEE/WIC/ACM international conference on web intelligence ACM, . Omaha, NE, pp 536–539

  114. Kim Y, Lee H, Jung K (2018) AttnConvnet at SemEval-2018 Task 1: attention-based convolutional neural networks for multi-label emotion classification. In: Proc of the 12th international workshop on semantic evaluation. Association for Computational Linguistics, New Orleans, pp 141–145

  115. Mulki H, Ali CB, Haddad H et al (2018) Tw-StAR at SemEval-2018 Task 1: preprocessing impact on multi-label emotion classification. In: Proc of the SemEval-2018. Association for Computational Linguistics, New Orleans, pp 167–171

  116. Alhuzali H, Ananiadou S (2021) SpanEmo: casting multi-label emotion classification as span-prediction. Comput Sci

  117. Hyun J, Bae B, Cheong Y (2020) [CL-AFF Shared Task] multi-label text classification using an emotion embedding model. In: Proc of the 3rd workshop of affective content analysis. CEUR Workshop Proceedings, New York, pp 169–178

  118. Ying W, Xiang R, Lu Q (2019) Improving multi-label emotion classification by integrating both general and domain-specific knowledge. In: Proc of the 5th workshop on noisy user-generated text. Association for Computational Linguistics, Hong Kong, pp 316–321

  119. Ameer I, Sidorov G, Gomez-Adorno H et al (2022) Multi-label emotion classification on code-mixed text: data and methods. IEEE Access 10:8779–8789

    Article  Google Scholar 

  120. Baumel T, Nassour-Kassis J, Cohen R, Elhadad M, Elhadad N (2018) Multi-label classification of patient notes: case study on ICD code assignment. AAAI Workshops, pp 409–416

  121. Du J, Chen Q, Peng Y, Xiang Y et al (2019) ML-Net: multi-label classification of biomedical texts with deep neural networks. J Am Med Inform Assoc 26(11):1279–1285

    Article  Google Scholar 

  122. Glinka K, Wozniak R, Zakrzewska D (2017) Improving multi-label medical text classification by feature selection. In: Proc of IEEE international conference on enabling technologies: infrastructure for collaborative enterprises. IEEE, Poznan, pp 176–181

  123. Hughes M, Li I, Kotoulas S, Suzumura (2017) Medical text classification using convolutional neural networks. Comput Sci

  124. Yogarajan V, Montiel J, Smith T, Pfahringer B (2020) Seeing the whole patient: using multi-label medical text classification techniques to enhance predictions of medical codes. Comput Sci

  125. Wasim M, Mahmood W, Asim MN et al (2019) Multi-label question classification for factoid and list type questions in biomedical question answering. IEEE Access 7:3882–3896

    Article  Google Scholar 

  126. D’Anniballe VM, Tushar FI, Faryna K et al (2022) Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. BMC Med Inform Decis Mak 22(1):1–12

    Article  Google Scholar 

  127. Oramas S, Nieto O, Barbieri F et al (2017) Multi-label music genre classification from audio, text, and images using deep features 23–30

  128. Zhao G, Xu J, Zeng Q et al (2019) Review-driven multi-label music style classification by exploiting style correlations. In: Proc of the 2019 conference of the north american chapter of the association for computational linguistics. Association for Computational Linguistics, Minneapolis, pp 2884–2891

  129. Ma Q, Yuan C, Zhou W, Han J, Hu S (2020) Beyond statistical relations: integrating knowledge relations into style correlations for multi-label music style classification. In: Proc of the 13th international conference on web search and data mining. ACM, Houston, pp 411–419

  130. Kim E, On K, Kim J et al (2018) Temporal attention mechanism with conditional inference for large-scale multi-label video classification. In: Proc of the European conference on computer vision. Lecture Notes in Computer Science, Munich, pp 306–316

  131. Karagoz GN, Yazici A, Dokeroglu T, Cosa A (2020) Analysis of multiobjective algorithms for the classification of multi-label video datasets. IEEE Access 8:163937–163952

    Article  Google Scholar 

  132. Araujo A, Negrevergne B, Chevaleyre Y et al (2018) Training compact deep learning models for video classification using circulant matrices. In: Proc of European conference on computer vision. Lecture Notes in Computer Science, Munich, pp 271–286

  133. Jiang B, Zhou L, Lin L et al (2019) A real-time multi-label classification system for short videos. In: Proc of 2019 IEEE international conference on image processing. IEEE, Taipei, pp 534–538

  134. Wu H, Li M, Liu Y et al (2022) Transtl: spatial-temporal localization transformer for multi-label video classification. In: ICASSP 2022–2022 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1965–1969

  135. Büyükçakır A, Bonab H, Can F (2018) A novel online stacked ensemble for multi-label stream classification. In: Proc of the 27th ACM international conference on information and knowledge management, pp 1063–1072

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (62062004), the Ningxia Natural Science Foundation Project (2022AAC03279).

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The National Nature Science Foundation of China (Grant no. 62062004); The Ningxia Natural Science Foundation Project (Grant no. 2022AAC03279).

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  1. School of Computer Science and Engineering, North Minzu University, Yinchuan, China

    Meng Han, Hongxin Wu, Zhiqiang Chen, Muhang Li & Xilong Zhang

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Han, M., Wu, H., Chen, Z. et al. A survey of multi-label classification based on supervised and semi-supervised learning. Int. J. Mach. Learn. & Cyber. 14, 697–724 (2023). https://doi.org/10.1007/s13042-022-01658-9

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