Cost-effective Batch-mode Multi-label Active Learning

A fast and elitist multiobjective genetic algorithm: NSGA-II

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Mining association rules between sets of items in large databases

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A Review on Multi-Label Learning Algorithms

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A survey on semi-supervised learning

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Learning multi-label scene classification

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Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

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Improved Heterogeneous Distance Functions

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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

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An analysis of active learning strategies for sequence labeling tasks

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The challenge problem for automated detection of 101 semantic concepts in multimedia

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Multi-Label Classification Of Music Into Emotions.

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Active Learning by Querying Informative and Representative Examples

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Linear Programming, Methods and Applications

Smith, E. C., & Gass, S. I. (1965) · Mathematics of Computation · doi:10.2307/2003382 · Cited by 429

A survey on instance selection for active learning

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A shared task involving multi-label classification of clinical free text

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SemiBoost: Boosting for Semi-Supervised Learning

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Multilabel Text Classification for Automated Tag Suggestion

Katakis, I., Vlahavas, I. P., & Tsoumakas, G. (2008) · Cited by 277

An Empirical Study of Lazy Multilabel Classification Algorithms

Spyromitros, E., Tsoumakas, G., & Vlahavas, I. P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87881-0_40 · Cited by 223

Effective multi-label active learning for text classification

Yang, B., Sun, J.-T., Wang, T., & Chen, Z. (2009) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557119 · Cited by 166

Linear Programming. Methods and Applications

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Active Learning: A Survey

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ActiveHNE: Active Heterogeneous Network Embedding

Chen, X., Yu, G., Wang, J., Domeniconi, C., Li, Z., & Zhang, X. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/294 · Cited by 97

Active learning with multi-label SVM classification

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Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion

Du, B., Wang, Z., Zhang, L., Zhang, L., & Tao, D. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2651372 · Cited by 87

A novel reasoning mechanism for multi-label text classification

Wang, R., Ridley, R., Su, X. H., Qu, W., & Dai, X. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102441 · Cited by 81

Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning

Huang, S.-J., & Zhou, Z. (2013) · Proceedings · doi:10.1109/icdm.2013.74 · Cited by 80

Effective active learning strategy for multi-label learning

Reyes, Ó., Morell, C., & Ventura, S. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.001 · Cited by 79

Ranking-Based Deep Cross-Modal Hashing

Liu, X., Yu, G., Domeniconi, C., Wang, J., Ren, Y., & Guo, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014400 · Cited by 78

Two-Dimensional Multilabel Active Learning with an Efficient Online Adaptation Model for Image Classification

Qi, G.-J., Hua, X., Rui, Y., Tang, J., & Zhang, H.-J. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.218 · Cited by 77

Batch Mode Active Sampling Based on Marginal Probability Distribution Matching

Chattopadhyay, R., Wang, Z., Fan, W., Davidson, I. A. K., Panchanathan, S., & Ye, J. (2013) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2513092.2513094 · Cited by 71

Learning label correlations for multi-label image recognition with graph networks

Li, Q., Peng, X., Qiao, Y., & Peng, Q. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.07.040 · Cited by 70

Active learning for sparse bayesian multilabel classification

Vasisht, D., Damianou, A., Varma, M., & Kapoor, A. (2014) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623759 · Cited by 68

Protein Function Prediction Using Multilabel Ensemble Classification

Yu, G., Rangwala, H., Domeniconi, C., Zhang, G., & Yu, Z. (2013) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2013.111 · Cited by 63

Multilabel Image Classification Via High-Order Label Correlation Driven Active Learning

Zhang, B., Wang, Y., & Chen, F. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2302675 · Cited by 58

Proceedings of the 1993 ACM SIGMOD international conference on Management of data

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Semi-supervised multi-label classification using incomplete label information

Tan, Q., Yu, Y., Yu, G., & Wang, J. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.04.033 · Cited by 53

Nearest Neighbor-Based Classification of Uncertain Data

Angiulli, F., & Fassetti, F. (2013) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2435209.2435210 · Cited by 46

Semi-supervised ensemble classification in subspaces

Yu, G., Zhang, G., Yu, Z., Domeniconi, C., You, J., & Han, G. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2011.12.019 · Cited by 44

Evolutionary Strategy to Perform Batch-Mode Active Learning on Multi-Label Data

Reyes, Ó., & Ventura, S. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3161606 · Cited by 44

A Comfort-Based Approach to Smart Heating and Air Conditioning

Auffenberg, F., Snow, S., Stein, S., & Rogers, A. (2017) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3057730 · Cited by 43

NewGOA: Predicting New GO Annotations of Proteins by Bi-Random Walks on a Hybrid Graph

Yu, G., Fu, G., Wang, J., & Zhao, Y. (2017) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2017.2715842 · Cited by 40

Active Multilabel Crowd Consensus

Yu, G., Tu, J., Wang, J., Domeniconi, C., & Zhang, X. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2984729 · Cited by 34

Linear programming methods and applications

Gass, S. I. (1958) · Cited by 34

Weak-Labeled Active Learning With Conditional Label Dependence for Multilabel Image Classification

Wu, J., Zhao, S., Sheng, V. S., Zhang, J., Ye, C., Zhao, P., & Cui, Z. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2652065 · Cited by 33

Multi-label active learning by model guided distribution matching

Gao, N., Huang, S.-J., & Chen, S. (2016) · Frontiers of Computer Science · doi:10.1007/s11704-016-5421-x · Cited by 30

Learning to rank for multi-label text classification: Combining different sources of information

Azarbonyad, H., Dehghani, M., Marx, M., & Kamps, J. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000029 · Cited by 30

Optimal batch selection for active learning in multi-label classification

Chakraborty, S., Balasubramanian, V. N., & Panchanathan, S. (2011) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2072298.2072028 · Cited by 21

CMAL: Cost-Effective Multi-Label Active Learning by Querying Subexamples

Yu, G., Chen, X., Domeniconi, C., Wang, J., Li, Z., Zhang, Z., & Zhang, X. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3003899 · Cited by 21

Active Learning With Optimal Instance Subset Selection

Fu, Y., Zhu, X., & Elmagarmid, A. K. (2012) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2209177 · Cited by 16

Cost Effective Multi-label Active Learning via Querying Subexamples

Chen, X., Yu, G., Domeniconi, C., Wang, J., Li, Z., & Zhang, Z. (2018) · Proceedings · doi:10.1109/icdm.2018.00109 · Cited by 16

Learning Class-Conditional GANs with Active Sampling

Xie, M.-K., & Huang, S.-J. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330883 · Cited by 13

Multi-label active learning based on submodular functions

Wu, K., Cai, D., & He, X. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.110 · Cited by 11

Multi-label Emotion Classification in Music Videos Using Ensembles of Audio and Video Features

Kostiuk, B., Costa, Y. M. G., Britto, A. S., Hu, X., & Junior, C. N. S. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00078 · Cited by 11

Multi-label active learning with low-rank mapping for image classification

Guo, A., Wu, J., Sheng, V. S., Zhao, P., & Cui, Z. (2017) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2017.8019412 · Cited by 9

Ten Years of Active Learning Techniques and Object Detection: A Systematic Review

Garcia, D., Carias, J., Adão, T., Jesus, R., Cunha, A., & Magalhães, L. G. (2023) · Applied Sciences · doi:10.3390/app131910667 · Cited by 17