Computer Science ›› 2022, Vol. 49 ›› Issue (8): 12-25.doi: 10.11896/jsjkx.210700111
• Database & Big Data & Data Science • Previous Articles Next Articles
WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang
CLC Number:
| [1]DU S Y,HAN M,SHEN M Y,et al.Survey of ensemble classification algorithms for data streams with concept drift [J].Computer Engineering,2020,46(1):15-24,30. [2]MA J,ZHANG H,CHOW T W S.Multilabel ClassificationWith Label-Specific Features and Classifiers:A Coarse and Fine Tuned Framework[J].IEEE Transactions on Cybernetics,2021,51(2):1028-1042. [3]HE H,XIA R.Joint Binary Neural Network for Multi-labelLearning with Applications to Emotion Classification[C]//Proceedings of the Natural Language Processing and Chinese Computing.2018:250-259. [4]DU J,CHEN Q,PENG Y,et al.ML-Net:multi-label classification of biomedical texts with deep neural networks[J].Journal of the American Medical Informatics Association,2019,26(11):1279-1285. [5]RANA P,MEIJERING E,SOWMYA A,et al.Multi-LabelClassification Based On Subcellular Region-Guided Feature Description For Protein Localisation[C]//Proceedings of the 18th International Symposium on Biomedical Imaging.France:IEEE,2021:1929-1933. [6]ORAMAS S,NIETO O,BARBIERI F,et al.Multi-label Music Genre Classification from Audio,Text,and Images Using Deep Features[C]//Proceedings of the 18th International Society for Music Information Retrieval Conference.2017:23-30. [7]KARAGOZ G N,YAZICI A,DOKEROGLU T,et al.Analysis of Multiobjective Algorithms for theClassification of Multi-Label Video Datasets[J].IEEE Access,2020,8:163937-163952. [8]READ J,PFAHRINGER B,HOLMES G,et al.Classifier chains for multi-label classification[J].Machine Learning,2011,85(3):333-359. [9]TSOUMAKAS G,VLAHAVAS I.Randomk-Labelsets:An Ensemble Method for Multilabel Classification[C]//Proceedings of the 18th European Conference on Machine Learning.Berlin:Springer,Warsaw:Lecture Notes in Computer Science,2007:406-417. [10]OLIVEIRA E,CIARELLI P M,BADUE C,et al.A Comparison between a KNN Based Approach and a PNN Algorithm for a Multi-label Classification Problem[C]//Proceedings of the Eighth International Conference on Intelligent Systems Design Applications.Kaohsiung:IEEE,2008:628-633. [11]ClARE A,KING R D.Knowledge Discovery in Multi-label Phenotype Data[C]//Proceedings of the European Conference on Principles of Data Mining and Knowledge Discovery.Freiburg:Lecture Notes in Computer Science,2001:42-53. [12]LI X,WANG L,SUNG E.Multi-label SVM active learning for image classification[C]//Proceedings of the Image Processing.Singapore:IEEE,2004:2207-2210. [13]SAPOZHNIKOVA E P.Multi-label Classification with ARTNeural Networks[C]//Proceedings of the 2009 Second International Workshop on Knowledge Discovery and Data Mining.Moscow:IEEE,2009:144-147. [14]ZHANG M,ZHOU Z.A Review on Multi-Label Learning Algorithms[J].IEEE Transactions on Knowledge & Data Enginee-ring,2014,26(8):1819-1837. [15]TSOUMAKAS G,KATAKIS I.Multi-Label Classification:An Overview[J].International Journal of Data Warehousing and Mining,2009,3(3):1-13. [16]MOYANO J M,GIBAJA E L,CIOS K J,et al.Review of Ensembles of Multi-Label Classifiers:Models,Experimental Study and Prospects[J].Information Fusion,2018,44:33-45. [17]ZHOU Z H,ZHANG M L.Multi-label Learning[J/OL].Encyclopedia ofMachine Learning and Data Mining,2017:875-881.https://link.springer.com/referenceworkentry/10.1007/978-1-4899-7687-1_910. [18]ZHENG X,LI P,CHU Z,et al.A Survey on Multi-Label Data StreamClassification[J].IEEE Access,2020,8:1249-1275. [19]LI P,WANG H,BHM C,et al.Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence.Yokohama,2020:1359-1365. [20]WANG Z,WANG T,WAN B,et al.Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification[J].Entropy,2020,22(10):1-22. [21]BEZEMBINDER E M,WISMANS L J J,BERKUM E C V.Constructing multi-labelled decision trees for junction design using the predicted probabilities[C]//Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems.Yokohama:IEEE,2017:1-7. [22]MORAL-GARCÍA S,MANTAS C J,CASTELLANO J G,et al.Non-parametric predictive inference for solving multi-label classification[J/OL].Applied Soft Computing,2020,88:106011.https://www.sciencedirect.com/science/article/pii/S1568494619307938?via%3Dihub. [23]MAJZOUBI M,CHOROMANSKA A.LdSM:Logarithm-depth Streaming Multi-label Decision Trees[C]//Proceedings of the the 23rd International Conference on Artificial Intelligence and Statistics.Palermo,2019:4247-4257. [24]YANG Y,DING M.Decision function with probability featureweighting based on Bayesian network for multi-label classification[J].Neural Computing and Applications,2019,31(9):4819-4828. [25]SUN Z,HU K,HU T,et al.Fast Multi-Label Low-Rank Linea-rized SVM Classification Algorithm Based on Approximate Extreme Points[J].IEEE Access,2018,6:42319-42326. [26]SUN Z,LIU X,HU K,et al.An Efficient Multi-Label SVM Classification Algorithm by Combining Approximate Extreme Points Method and Divide-and-Conquer Strategy[J].IEEE Access,2020,8:170967-170975. [27]BELLMANN F,BUNZEL L,DEMUS C,et al.Multi-LabelClassification of Blurbs with SVM Classifier Chains[C]//Proceedings of the 15th Conference on Natural Language Proces-sing.2019. [28]WANG X,GUO Z,WANG X,et al.A FastNeural NetworkMulti-label Classification Algorithm Based on Approximate Extreme Points[C]//Proceedings of the 5th International Confe-rence on Big Data Computing and Communications.QingDao:IEEE,2019:17-21. [29]BELLO M,GONZALO N,RICARDO S,et al.Deep neural network to extract high-level features and labels in multi-label classification problems[J].Neurocomputing,2020,413:259-270. [30]SUMBUL G,BEGÜM D.A Deep Multi-Attention Driven Approach for Multi-Label Remote Sensing Image Classification[J].IEEE Access,2020,8:95934-95946. [31]WEI X,YU Z,ZHANG C,et al.Ensemble of Label Specific Features for Multi-Label Classification[C]//Proceedings of the 2018 IEEE International Conference on Multimedia and Expo.San Diego.IEEE,2018:1-6. [32]MORAL-GARCÍA S,MANTAS C J,CASTELLANO J G,et al.Ensemble of classifier chains and Credal C4.5 for solving multi-label classification[J].Progress in Artificial Intelligence,2019,8(2):195-213. [33]LOTF H,RAMDANI M.Multi-Label Classification:A Novelapproach using decision trees for learning Label-relations and preventing cyclical dependencies:Relations Recognition and Removing Cycles (3RC)[C]//Proceedings of the 13th InternationalConference on Intelligent Systems:Theories and Applications.Rabat,ACM,2020:1-6. [34]KIM H,PARK J,KIM D,et al.Multilabel naÏve Bayes classification considering label dependence[J].Pattern Recognition Letters,2020,136:279-285. [35]ZHANG Z D,WANG Z H,LIU H Y.An Integrated Multi-label Classification Algorithm Based on Tree Bayesian Network[J].Computer Science,2018,45:195-201. [36]JIMÉNEZ R B,MORALES E F,ESCALANTE H J.BayesianChain Classifier with Feature Selection for Multi-label Classification[C]//Proceedings of the Mexican International Confe-rence on Artificial Intelligence.2018:232-243. [37]WU G,ZHENG R,TIAN Y,et al.Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification[J].Neural Networks,2020,122:24-39. [38]KODA S,ZEGGADA A,MELGANI F,et al.Spatial and Structured SVM for Multilabel Image Classification[J].IEEE Transa-ctions on Geoscience and Remote Sensing,2018,56(10):5948-5960. [39]CHEN B,LI J,LU G,et al.Label Co-occurrence Learning with Graph Convolutional Networks for Multi-label Chest X-ray Image Classification[J].IEEE Journal of Biomedical and Health Informatics,2019,24(8):2292-2302. [40]LI J,FU G,CHEN Y,et al.A multi-label classification model for full slice brain computerised tomography image[J].BMC Bioinformatics,2020,21(6):1-18. [41]HE J,LI C,YE J,et al.Multi-label ocular disease classificationwith a dense correlation deep neural network[J].Biomedical Signal Processing and Control,2021,63:102167. [42]BRAYTEE A,LIU W,ANAISSI A,et al.Correlated Multi-label Classification with Incomplete Label Space and Class Imbalance[J].ACM Transactions on Intelligent Systems and Technology,2019,10(5):1-26. [43]WANG R,KWONG S,JIA Y,et al.Active k-labelsets ensemble for multi-labelclassification[J/OL].Pattern Recognition,2021,109.https://www.sciencedirect.com/science/article/pii/S0031320320303861?via%3Dihub. [44]WANG R,KWONG S,JIA Y,et al.Mutual Information Based K-Labelsets Ensemble forMulti-Label Classification[C]//Proceedings of the 2018 IEEE International Conference on Fuzzy Systems.Rio de Janeiro:IEEE,2018:1-7. [45]BOULBAZINE S,CABANES G,MATEI B,et al.Online Semi-supervised Growing Neural Gas for Multi-label Data Classification[C]//Proceedings of the 2018 International Joint Confe-rence on Neural Networks.Rio de Janeiro:IEEE,2018:1-8. [46]CEVIKALP H,BENLIGIRAY B,GEREK O N.Semi-Super-vised Robust Deep Neural Networks for Multi-Label Image Classification[J].Pattern Recognition,2019,100:107164. [47]LI S,FU Y.Robust multi-label semi-supervised classification[C]//Proceedings of the 2017 IEEE International Conference on Big Data.Boston:IEEE,2017:27-36. [48]LIU Y,NIE F,GAO Q X.Nuclear-norm Based Semi-supervised Multiple Labels Learning[J/OL].Neurocomputing,2017:940-947.https://linkinghub.elsevier.com/retrieve/pii/S0925231217315588. [49]AMORIM W P,FALCAO A X,PAPA J P.Multi-label semi-supervised classification through optimum-path forest[J].Information Sciences,2018,465:86-104. [50]ABBURI H,PARIKH P,CHHAYA N,et al.Fine-grainedMulti-label Sexism Classification Using Semi-supervised Lear-ning[C]//Proceedings of the 21st International Conference Amsterdam,Amsterdam:Lecture Notes in Computer Science.2020:531-547. [51]XU Z,ZHAI Y,LIU Y.Distributed Semi-supervised Multi-label Classification with Quantized Communication[C]//Proceedings of the 12th International Conference on Machine Learning and Computing.Shenzhen:ACM,2020:57-62. [52]JING L,SHEN C,YANG L,et al.Multi-Label Classification by Semi-Supervised Singular Value Decomposition[J].IEEE Transa-ctions on Image Processing,2017,26(10):4612-4625. [53]QIU Y,GONG X,MA Z.MixLab:An Informative Semi-supervised Methodfor Multi-label Classification[C]//Proceedings of the 9th CCF International Conference.Zhengzhou:Lecture Notes in Computer Science,2020:506-518. [54]LI P,WANG H,BHM C,et al.Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence.Yokohama,2020:1359-1365. [55]AHMADI Z,KRAMERS.A label compression method for online multi-label classification[J].Pattern Recognition Letters,2018,11:64-71. [56]CHEN N,RIBEIRO B,TANG C,et al.Multi-label learning vector quantization for semi-supervised classification[J].Intelligent Data Analysis,2019,23(4):839-853. [57]WANG X P,GUO Z W,WANG X,et al.NNMLInf:social influen-ce prediction with neural network multi-label classification[C]//Proceedings of the ACM Turing Celebration Conference.Chengdu:ACM,2019,106:1-5. [58]ZHAN W,ZHANG M L.Inductive Semi-supervised Multi-Label Learning with Co-Training[C]//Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery & Data Mi-ning.Halifax:ACM,2017:1305-1314. [59]XING Y,YU G,DOMENICONI C,et al.Multi-Label Co-Trai-ning[C]//Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence.Stockholm,2018:2882-2888. [60]CHU Z,LI P,HU X.Co-training Based on Semi-Supervised Ensemble Classification Approach for Multi-label DataStream[C]//Proceedings of the 2019 IEEE International Conference on Big Knowledge.Beijing:IEEE,2019:58-69. [61]LEE C H.Multi-label classification of documents using fine-grained weights and modified co-training[J].Intelligent Data Analysis,2018,22(1):103-115. [62]TAN Q,YU Y,YU G,et al.Semi-supervised multi-label classification using incomplete label information[J].Neurocompu-ting,2017,260:192-202. [63]CHAUDHURI B,DEMIR B,CHAUDHURI S,et al.Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(2):1144-1158. [64]GUAN Q,HUANG Y.Multi-label Chest X-ray Image Classification via Category-wise Residual Attention Learning[J].Pattern Recognition Letters,2018,130:259-266. [65]CAI J,SUN W,GUAN J,et al.Multi-ECGNet for ECG Arrythmia Multi-Label Classification[J].IEEE Access,2020,8:110848-110858. [66]MERCAN C,AKSOY S,MERCAN E,et al.Multi-InstanceMulti-LabelLearning for Multi-Class Classification of Whole Slide Breast Histopathology Images[J].IEEE Trans. Med. Imaging,2017,37(1):316-325. [67]XU S,YANG X,GUO J,et al.CXNet-m3:a deep quintupletnetwork for multi-lesion classification in Chest X-ray Images via multi-label supervision[J].IEEE Access,2020,8:98693-98704. [68]HUA Y,MOU L,ZHU X X.Relation Networkfor MultilabelAerial Image Classification[J].IEEE Transactions on Geo-science and Remote Sensing,2020,58(7):4558-4572. [69]ALSHEHRI A,BAZI Y,AMMOUR N,et al.Deep Attention Neural Network for Multi-label Classification in Unmanned Aerial Vehicle Imagery[J].IEEE Access,2019,7:119873-119880. [70]DAI O E,DEMIR B,SANKUR B,et al.A Novel System forContent-Based Retrieval of Single and Multi-Label High-Dimensional Remote Sensing Images[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11(7):2473-2490. [71]YU J,LUÍS M,JIANG J,et al.Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.2018:1097-1102. [72]FEI H,JI D,ZHANG Y,et al.Topic-Enhanced Capsule Net-work for Multi-Label Emotion Classification[J].IEEE/ACM Transactions on Audio,Speech,and Language Processing,2020,28:1839-1848. [73]WEN W,CHEN Y,CAI R.Reader sentiment classificationbased on multi-perspective and multi-label learning[J].Compu-ter Science,2018,45(8):198-204. [74]ALZU’BI S,BADARNEH O,HAWASHIN B,et al.Multi-Label Emotion Classification for Arabic Tweets[C]//Proceedings of the 2019 Sixth International Conference on Social Networks Analysis.Granada:IEEE,2019:499-504. [75]BRAVO-MARQUEZ F,FRANK E,MOHAMMAD S M,et al.Determining Word-Emotion Associations from Tweets by Multi-label Classification[C]//Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence.Omaha,NE:ACM,2016:536-539. [76]KIM Y,LEE H,JUNG K.AttnConvnet at SemEval-2018 Task 1:Attention-based Convolutional Neural Networks for Multi-label Emotion Classification[C]//Proceedings of the 12th International Workshop on Semantic Evaluation.New Orleans:Association for Computational Linguistics,2018:141-145. [77]MULKI H,ALI C B,HADDAD H,et al.Tw-StAR at SemEval-2018 Task 1:Preprocessing Impact on Multi-label Emotion Classification[C]//Proceedings of the SemEval-2018.New Orleans:Association for Computational Linguistics,2018:167-171. [78]ALHUZALI H,ANANIADOU S.SpanEmo:Casting Multi-label Emotion Classification as Span-prediction[C]//Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics.2021:1573-1584. [79]HYUN J,BAE B,CHEONG Y.[CL-AFF Shared Task] Multi-label Text Classification Using an Emotion Embedding Model[C]//Proceedings of the 3rd Workshop of Affective Content Analysis.New York:CEUR Workshop Proceedings,2020:169-178. [80]YING W,XIANG R,LU Q.Improving Multi-label EmotionClassification by Integrating both General and Domain-specific Knowledge[C]//Proceedings of the 5th Workshop on Noisy User-generated Text.Hong Kong:Association for Computational Linguistics,2019:316-321. [81]BAUMEL T,NASSOUR-KASSIS J,COHEN R,et al.Multi-Label Classification of Patient Notes:Case Study on ICD Code Assignment[C]//Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence.2018:409-416. [82]GLINKA K,WOZNIAK R,ZAKRZEWSKA D.ImprovingMulti-label Medical Text Classification by Feature Selection[C]//Proceedings of IEEE International Conference on Enabling Technologies:Infrastructure for Collaborative Enterprises.Poznan:IEEE,2017:176-181. [83]HUGHES M,LI I,KOTOULAS S,et al.Medical Text Classification using Convolutional Neural Networks[J/OL].Computer Science,2017.https:// arxiv.org/abs/1704.06841. [84]YOGARAJAN V,MONTIEL J,SMITH T,et al.Seeing TheWhole Patient:Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes[J/OL].Computer Science,2020.https://arxiv.org/abs/ 2004.00430. [85]WASIM M,MAHMOOD W,ASIM M N,et al.Multi-LabelQuestion Classification for Factoid and List Type Questions in Biomedical Question Answering[J].IEEE Access,2019,7:3882-3896. [86]ZHAO G,XU J,ZENG Q,et al.Review-Driven Multi-LabelMusic Style Classification by Exploiting Style Correlations[C]//Proceedings of the 2019 Conference of the North American Chapter.Minneapolis:Association for Computational Linguistics,2019:2884-2891. [87]MA Q,YUAN C,ZHOU W,et al.Beyond Statistical Relations:Integrating Knowledge Relations into Style Correlations for Multi-Label Music Style Classification[C]//Proceedings of the 13th International Conference on Web Search and Data Mining.Houston:ACM,2020:411-419. [88]KIM E,ON K,KIM J,et al.Temporal Attention Mechanism with Conditional Inference for Large-Scale Multi-label Video Classification[C]//Proceedings of the European Conference on Computer Vision.Munich:Lecture Notes in Computer Science,2018:306-316. [89]ARAUJO A,NEGREVERGNE B,CHEVALEYRE Y,et al.Training Compact Deep Learning Models for Video Classification Using Circulant Matrices[C]//Proceedings of European Conference on Computer Vision.Munich:Lecture Notes in Computer Science,2018:271-286. [90]JIANG B,ZHOU L,LIN L,et al.A Real-Time Multi-LabelClassification System for Short Videos[C]//Proceedings of 2019 IEEE International Conference on Image Processing.Taipei:IEEE,2019:534-538. [91]PEREIRA R B,PLASTINO A,ZADROZNY B,et al.Correlation analysis of performance measures for multi-label classification[J].Information Processing & Management,2018,54(3):359-369. [92]BÜYÜKÇAKIR A,BONAB H,CAN F.A Novel Online Stacked Ensemble for Multi-Label Stream Classification[C]//Procee-dings of the 27th ACM International Conference on Information and Knowledge Management.2018:1063-1072. [93]LU Y X,LIU B N,WANG C R,et al.Adaptive incremental ensemble classification algorithm for concept drift data stream[J].Small Microcomputer System,2019,40(12):2624-2630. [94]LIU J,LIN Y,LI Y,et al.Online multi-label streaming feature selection based on neighborhoodrough set[J].Pattern recognition,2018,84:273-287. |
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