Computer Science ›› 2020, Vol. 47 ›› Issue (3): 192-199.doi: 10.11896/jsjkx.190300137

• Artificial Intelligence • Previous Articles     Next Articles

Research and Development of Multi-label Generation Based on Deep Learning

LIU Xiao-ling,LIU Bai-song,WANG Yang-yang,TANG Hao   

  1. (Faculty of Information Science and Technology, Ningbo University, Ningbo, Zhejiang 315211, China)
  • Received:2019-03-26 Online:2020-03-15 Published:2020-03-30
  • About author:LIU Xiao-ling, born in 1994,postgra-duate.Her main research interests include natural language processing and data mining. LIU Bai-song,born in 1971,Ph.D,researcher,Ph.D supervisor,is member of China Computer Federation.His main research interests include natural language processing,big data and artificial intelligence.
  • Supported by:
    This work was supported by the National Social Science Foundation of China (15FTQ002).

Abstract: In the era of big data,data show the characteristics of high dimension,large amount and rapid growth.Efficiently discovering knowledge from these data is a research focus.Multi-label has been proposed for ambiguous objects in reality,and is widely used in data intelligent processing.In recent years,Multi-label generation receives widespread attention due to the excellent performance of deep learning.The latest research results were summarized from five categories and were further compared and analyzed from the aspects of data,relationship types,application scene,adaptability and experimental performance.Finally,the challenges of multi-label generation were discussed,followed with the prospects for future work.

Key words: Deep learning, Label correlation, Multi-label learning, Neural networks

CLC Number: 

  • TP391
[1]ALAZAIDAH R,THABTAH F,AL-RADAIDEH Q.A Multi-label Classification Approach Based on Correlations Among Labels[J].International Journal of Advanced Computer Science and Applications,2015,6(2):52-59.
[2]CORANI G,SCANAGATTA M.Air Pollution Prediction via Multi-label Classification[J].Environmental Modelling & Software,2016,80(6):259-264.
[3]ZHANG M L,WU L.Lift:Multi-label learning with label-spe- cific features[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(1):107-120.
[4]SONG Z,QIU Q.Learn to Classify and Count:A Unified Framework for Object Classification and Counting[C]∥Proceedings of the 2018 International Conference on Image and Graphics Processing.ACM,2018:110-114.
[5]HUANG C Q,YANG S M,PAN Y,et al.Object-location-aware hashing for multi-label image retrieval via automatic mask lear-ning[J].IEEE Transactions on Image Processing,2018,27(9):4490-4502.
[6]LI Z,LIAO B,LI Y,et al.Gene Function Prediction Based on Combining Gene Ontology Hierarchy with Multi-instance Multi-label Learning[J].Rsc Advances,2018,8(50):28503-28509.
[7]CAKIR E,HEITTOLA T,HUTTUNEN H,et al.Polyphonic sound event detection using multi label deep neural networks[C]∥International Joint Conference on Neural Networks.IEEE,2015:1-7.
[8]ZHANG C.Multi-label Text Categorization of Zhi Hu Title Based on Deep Learning[D].Beijing:Beijing Jiaotong University,2018.
[9]SUN J J,PEI L,JIANG T.Research on Semantic Annotation in Academic Literature[J].Journal of the China Society for Scientific and Technical Information,2018,37(11):1077-1086.
[10]ZHANG M L,ZHOU Z H.A review on multi-label learning algorithms[J].IEEE transactions on knowledge and data engineering,2014,26(8):1819-1837.
[11]ZHANG J J.Research on Key Problems of Multi-label Learning[D].Xi’an:Xidian University,2016.
[12]SPOLAÔR N,MONARD M C,TSOUMAKAS G,et al.A systematic review of multi-label feature selection and a new method based on label construction[J].Neurocomputing,2016180(C):3-15.
[13]WANG S B,LI Y F.Classifier Circle Method for Multi-Label Learning[J].Journal of Software,2015,26(11):2811-2819.
[14]PENG Y.Research on multi-label learning problems based on topic model[D].Nanjing:Nanjing University,2018.
[15]FU D,ZHOU B,HU J.Improving SVM based multi-label classification by using label relationship[C]∥2015 International Joint Conference on Neural Networks (IJCNN).IEEE,2015:1-6.
[16]YAN Y.Text Representation and Classification with Deep Learning[D].Beijing:University of Science & Technology Beijing,2016.
[17]LI S L,LIU R,LIU H.Multi-label Learning for Improved RBF Neural Networks[J].Computer Science,2015,42(4):316-320.
[18]ABDECHIRI M,FAEZ K.Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks[J].Applied Soft Computing,2015,34(C):788-798.
[19]ZHANG N,DING S,ZHANG J.Multi-Layer Elmrbf for Multi-label Learning[J].Applied Soft Computing,2016,43(C):535-545.
[20]LI G,NIU P,DUAN X,et al.Fast learning network:a novel artificial neural network with a fast learning speed[J].Neural Computing and Applications,2014,24(7/8):1683-1695.
[21]DANG D,XU X Z.Multi-label learning Model Based on Radial Basis Function Neural Network and Regularized Extreme Learning Machine[J].Pattern Recognition and Artificial Intelligence,2017,30(9):67-74.
[22]Y YEH C K,WU W C,KO W J,et al.Learning deep latent space for multi-label classification[C]∥Thirty-First AAAI Conference on Artificial Intelligence.AAAI,2017:2838-2844.
[23]NAM J,KIM J,MENCíA E L,et al.Large-scale multi-label text classification revisiting neural networks[C]∥Machine Learning and Knowledge Discovery in Databases-European Conference.Springer,2014:437-452.
[24]XIE P,SALAKHUTDINOV R,MOU L,et al.Deep determinantal point process for large-scale multi-label classification[C]∥Proceedings of the IEEE International Conference on Computer Vision.IEEE Computer Society,2017:473-482.
[25]KURATA G,XIANG B,ZHOU B.Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence[C]∥Proceedings of the 2016 Confe-rence of the North American Chapter of the Association for Computational Linguistics.ACL,2016:521-526.
[26]HÜLLERMEIER E,CHENG W.Superset Learning Based on Generalized Loss Minimization[C]∥Joint European Conference on Machine Learning and Knowledge Discovery in Databases.Springer,2015:260-275.
[27]LIU J,CHANG W C,WU Y,et al.Deep learning for extreme multi-label text classification[C]∥Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2017:115-124.
[28]CHEN G,YE D,XING Z,et al.Ensemble application of convolutional and recurrent neural networks for multi-label text categorization[C]∥2017 International Joint Conference on Neural Networks.IEEE,2017:2377-2383.
[29]ZHAO B,LI X,LU X,et al.A CNN-RNN architecture for multi-label weather recognition[J].Neurocomputing,2018(12),322:47-57.
[30]YUAN Y,XUN G,MA F,et al.A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning[C]∥2018 IEEE EMBS International Conference on Biomedical & Health Informatics.IEEE,2018:206-209.
[31]SONG P,JING L P.Exploiting Label Relationships in Multi-Label Classification with Neural Networks[J].Journal of ComputerResearch and Development,2018,55(8):1751-1759.
[32]GENG X,XU N.Label distribution learning and label enhancement[J].Scientia Sinica Informationis,2018,48(5):521-530.
[33]GAO B B,XING C,XIE C W,et al.Deep label distribution learning with label ambiguity[J].IEEE Transactions on Image Processing,2017,26(6):2825-2838.
[34]LIU W,WEN Y,YU Z,et al.Large-Margin Softmax Loss for Convolutional Neural Networks [C]∥Proceedings of the 33nd International Conference on Machine Learning.JMLR,2016:507-516.
[35]DURAND T,THOME N,CORD M.Weldon:Weakly super- vised learning of deep convolutional neural networks[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:4743-4752.
[36]WU B,LYU S,GHANEM B.Constrained submodular minimization for missing labels and class imbalance in multi-label lear-ning[C]∥Thirtieth AAAI Conference on Artificial Intelligence.AAAI,2016:2229-2236.
[37]WU B,JIA F,LIU W,et al.Multi-label learning with missing labels using mixed dependency graphs[J].International Journal of Computer Vision,2018,126(8):875-896.
[38]WANG M,FU W,HAO S,et al.Scalable semi-supervised lear- ning by efficient anchor graph regu-larization[J].IEEE Transactions on Knowledge and Data Engineering,2016,28(7):1864-1877.
[39]JING L,YANG L,YU J,et al.Semi-supervised low-rank mapping learning for multi-label classification[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2015:1483-1491.
[40]LIN G,LIAO K,SUN B,et al.Dynamic Graph Fusion Label Propagation for Semi-supervised Multi-modality Classification[J].Pattern Recognition,2017,68(8):14-23.
[41]WU F,WANG Z,ZHANG Z,et al.Weakly semi-supervised deep learning for multi-label image annotation[J].IEEETransa-ctions on Big Data,2015,1(3):109-122.
[42]PAPANIKOLAOU Y,TSOUMAKAS G,KATAK-IS I.Hierarchical partitioning of the output space in multi-label data[J].Data & Knowledge Engineering,2018,116(7):42-60.
[43]CHU H M,YEH C K,WANG Y C.Deep Generative Models for Weakly-Supervised Multi-Label Classification[C]∥Proceedings of the European Conference on Computer Vision (ECCV).Springer,2018:400-415.
[44]WEI Y,LIANG X,CHEN Y,et al.Stc:A simple to complex framework for weakly-supervised semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(11):2314-2320.
[45]LI Y,YANG J,SONG Y,et al.Learning from noisy labels with distillation[C]∥Proceedings of the IEEE International Confe-rence on Computer Vision.IEEE Computer Society,2017:1910-1918.
[46]ZHAO G,XU J,ZENG Q,et al.Driven Multi-label Music Style Classification by Exploiting Style Correlations[J].arXiv:1808.07604,2018.
[47]ZHU F,LI H,OUYANG W,et al.Learning spatial regularization with image-level supervisions for multi-label image classification[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2017:5513-5522.
[48]VINYALS O,BENGIO S,KUDLUR M.Order matters:Se- quence to sequence for sets [J].arXiv:1511.06391,2015.
[49]CHEN S F,CHEN Y C,YEH C K,et al.Order-free rnn with visual attention for multi-label classification[C]∥Thirty-Se-cond AAAI Conference on Artificial Intelligence.AAAI,2018:6714-6721.
[50]WEI J,PHAM K,O′CONNOR B,et al.Evaluating Grammaticality in Seq2seq Models with a Broad Coverage HPSG Grammar:A Case Study on Machine Translation[C]∥Conference on Empirical Methods in Natural Language Processing.ACL,2018:298-305.
[51]HU X,LI G,XIA X,et al.Deep code comment generation[C]∥Proceedings of the 26th Conference on Program Comprehension.ACM,2018:200-210.
[52]FU Z,TAN X,PENG N,et al.Style transfer in text:Exploration and evaluation[C]∥Thirty-Second AAAI Conference on Artificial Intelligence.AAAI,2018:663-670.
[53]AKAMA R,INADA K,INOUE N,et al.Generating stylistically consistent dialog responses with transfer learning[C]∥Procee-dings of the Eighth International Joint Conference on Natural Language Processing.2017:408-412.
[54]YANG P,SUN X,LI W.SGM:sequence generation model for multi-label classification[C]∥Proceedings of the 27th International Conference on Computational Linguistics.2018:3915-3926.
[55]LIN J,SU Q,YANG P,et al.Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification[C]∥Proceedings of the 2018 Conference on Empirical Methods in Natural Language Procesing.ACL,2018:4554-4564.
[56]GEHRING J,AULI M,GRANGIER D,et al.Convolutional sequence to sequence learning[C]∥Proceedings of the 34th International Conference on Machine Learning-Volume 70.JMLR,2017:1243-1252.
[57]LI W,REN X,DAI D,et al.Sememe prediction:Learning semantic knowledge from unstructured textual wiki descriptions[J].arxiv:1808.05437,2018.
[58]HE D,XIA Y,QIN T,et al.Dual learning for machine translation[C]∥Advances in Neural Information Processing Systems.MIT Press,2016:820-828.
[59]BAHDANAU D,BRAKEL P,XU K,et al.An actor-critic algorithm for sequence prediction[J].arXiv:1607.07086,2016.
[60]YIN Q,ZHANG Y,ZHANG W,et al.Deep reinforcement learning for chinese zero pronoun resolution[C]∥Annual Mee-ting of the Association for Computational Linguistics.ACL,2018:569-578.
[61]YANG P,MA S,ZHANG Y,et al.A Deep Reinforced Sequence-to-Set Model for Multi-Label Text Classification[J].arXiv:1809.03118,2018.
[62]ZHAO L,ZHANG C,ZHANG X,et al.A Deep Reinforced Training Method for Location-Based Image Captioning[C]∥Pacific Rim International Conference on Artificial Intelligence.Springer,2018:878-890.
[63]HE S,XU C,GUO T,et al.Reinforced Multi-Label Image Classification by Exploring Curriculum [C]∥Thirty-Second AAAI Conference on Artificial Intelligence.AAAI,2018:3183-3190.
[64]CHEN T,WANG Z,LI G,et al.Recurrent attentional reinforcement learning for multi-label image recognition[C]∥Thirty-Second AAAI Conference on Artificial Intelligence.AAAI,2018:6730-6737.
[65]TSAI C P,LEE H Y.Adversarial Learning of Label Dependency:A Novel Framework for Multi-class Classification[J].arXiv:1811.04 689,2018.
[66]ZHOU T,LI Z,ZHANG C,et al.An Improved Convolutional Neural Network Model with Adversarial Net for Multi-label Image Classification[C]∥Pacific Rim International Conference on Artificial Intelligence.Springer,2018:38-46.
[67]MEN K,BOIMEL P,JANOPAUL NAYLOR J,et al.Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy[J].Physics in Medicine & Biology,2018,63(18):185016.
[68]BEHPOUR S.Arc:Adversarial robust cuts for semi-supervised and multi-label classification[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2018:1905-1907.
[69]ZHOU Z H.Learn ware:on the future of machine learning [J].Frontiers of Computer Science,2016,10(4):589-590.
[70]JIANG S,XU Y,WANG T.Multi-Label Metric Transfer Lear- ning Jointly Considering Instance Space and Label Space Distribution Divergence[J].IEEE Access,2019,7:10362-10373.
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