{"type":"ScholarlyArticle","title":"Multi-instance multi-label active learning","authors":[{"name":"Sheng-Jun Huang","slug":"sheng-jun-huang","given":"Sheng-Jun","family":"Huang","affiliation":"College of Computer Science & Technology , Nanjing University of Aeronautics & Astronautics; Collaborative Innovation Center of Novel Software Technology and Industrialization"},{"name":"Nengneng Gao","slug":"nengneng-gao","given":"Nengneng","family":"Gao","affiliation":"College of Computer Science & Technology , Nanjing University of Aeronautics & Astronautics; Collaborative Innovation Center of Novel Software Technology and Industrialization"},{"name":"Songcan Chen","slug":"songcan-chen","given":"Songcan","family":"Chen","affiliation":"College of Computer Science & Technology , Nanjing University of Aeronautics & Astronautics; Collaborative Innovation Center of Novel Software Technology and Industrialization"}],"abstract":"Multi-instance multi-label learning(MIML) has been successfully applied into many real-world applications. Along with the enhancing of the expressive power, the cost of labelling a MIML example increases significantly. And thus it becomes an important task to train an effective MIML model with as few labelled examples as possible. Active learning, which actively selects the most valuable data to query their labels, is a main approach to reducing labeling cost. Existing active methods achieved great success in traditional learning tasks, but cannot be directly applied to MIML problems. In this paper, we propose a MIML active learning algorithm, which exploits diversity and uncertainty in both the input and output space to query the most valuable information. This algorithm designs a novel query strategy for MIML objects specifically and acquires more precise information from the oracle without addition cost. Based on the queried information, the MIML model is then effectively trained by simultaneously optimizing the relative rank among instances and labels.","doi":"10.24963/ijcai.2017/262","url":"https://ijcai.org/proceedings/2017/262","pdf":"https://ijcai.org/proceedings/2017/0262.pdf","markdown":"https://ijcai.org/proceedings/2017/262.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/262","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":262,"pages":{"first":1886,"last":1892},"keywords":["Machine Learning: Semi-Supervised Learning","Machine Learning: Active Learning","Machine Learning: Multi-instance/Multi-label/Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Balcan et al., 2007] M.-F. Balcan, A. Z. Broder, and T. Zhang. Margin based active learning. In Proceedings of the 20th Annual Conference on Learning Theory, pages 35-50, 2007.","title":"Margin based active learning","authors":"M.-F Balcan, A Z Broder, T Zhang","year":"2007","venue":"Proceedings of the 20th Annual Conference on Learning Theory","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Briggs et al., 2012] F. Briggs, X. Z. Fern, and R. Raich. Rank-loss support instance machines for MIML instance annotation. In Proceedings of the 18th ACM SIGKDD In- ternational Conference on Knowledge Discovery and Data Mining, pages 534-542, 2012.","title":"Rank-loss support instance machines for MIML instance annotation","authors":"F Briggs, X Z Fern, R Raich","year":"2012","venue":"Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Feng and Xu, 2010] S. Feng and D. Xu. Transductive multi- instance multi-label learning algorithm with application to automatic image annotation. Expert Systems with Applica- tions, 37(1):661-670, 2010.","title":"Transductive multiinstance multi-label learning algorithm with application to automatic image annotation","authors":"S Feng, D Xu","year":"2010","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Huang and Zhou, 2013] S.-J. Huang and Z.-H. Zhou. Ac- tive query driven by uncertainty and diversity for incre- mental multi-label learning. In Proceedings of the 13th IEEE International Conference on Data Mining, pages 1079-1084, 2013.","title":"Active query driven by uncertainty and diversity for incremental multi-label learning","authors":"S.-J Huang, Z.-H Zhou","year":"2013","venue":"Proceedings of the 13th IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Huang et al., 2014a] S.-J. Huang, W. Gao, and Z.-H. Zhou. Fast multi-instance multi-label learning. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, pages 1868-1874, 2014.","title":"Fast multi-instance multi-label learning","authors":"Huang","year":"2014","venue":"Proceedings of the 28th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Huang et al., 2014b] S.-J. Huang, R. Jin, and Z.-H. Zhou. Active learning by querying informative and representa- tive examples. IEEE Transactions on Pattern Analysis and Machine Intelligence, (10):1936-1949, 2014.","title":"Active learning by querying informative and representative examples","authors":"Huang","year":"2014","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Huang et al., 2015] S.-J. Huang, S. Chen, and Z.-H. Zhou. Multi-label active learning: query type matters. In Pro- ceedings of the 24th International Joint Conference on Ar- tificial Intelligence, pages 946-952, 2015.","title":"Multi-label active learning: query type matters","authors":"S.-J Huang, S Chen, Z.-H Zhou","year":"2015","venue":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/138"},{"position":8,"raw":"[Hung and Lin, 2011] C.-W. Hung and H.-T. Lin. Multi- label active learning with auxiliary learner. In Proceedings of the 3rd Asian Conference on Machine Learning, pages 315-330, 2011.","title":"Multilabel active learning with auxiliary learner","authors":"C.-W Hung, H.-T Lin","year":"2011","venue":"Proceedings of the 3rd Asian Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Li and Guo, 2013] X. Li and Y. Guo. Active learning with multi-label svm classification. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pages 1479-1485, 2013.","title":"Active learning with multi-label svm classification","authors":"Guo Li, X Li, Y Guo","year":"2013","venue":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2013/221"},{"position":10,"raw":"[Li et al., 2004] X. Li, L. Wang, and E. Sung. Multi-label SVM active learning for image classification. In Proceed- ings of the 2004 International Conference on Image Pro- cessing, pages 2207-2210, 2004.","title":"Multi-label SVM active learning for image classification","authors":"Li","year":"2004","venue":"Proceedings of the 2004 International Conference on Image Processing","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Li et al., 2012] Y.-X. Li, S. Ji, S. Kumar, J. Ye, and Z.- H. Zhou. Drosophila gene expression pattern annotation through multi-instance multi-label learning. IEEE/ACM Transactions on Computational Biology and Bioinformat- ics, 9(1):98-112, 2012.","title":"Drosophila gene expression pattern annotation through multi-instance multi-label learning","authors":"Y.-X Li, S Ji, S Kumar, J Ye, Z.-H Zhou","year":"2012","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Lin et al., 2016] C. H. Lin, M. Mausam, and D. S. Weld. Re-active learning: Active learning with relabeling. In Proceedings of the 30th AAAI Conference on Artificial In- telligence, pages 1845-1852, 2016.","title":"Re-active learning: Active learning with relabeling","authors":"C H Lin, M Mausam, D S Weld","year":"2016","venue":"Proceedings of the 30th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Nguyen and Smeulders, 2004] H. T. Nguyen and A. W. M. Smeulders. Active learning using pre-clustering. In Pro- ceedings of the 21st International Conference on Machine Learning, 2004.","title":"Active learning using pre-clustering","authors":"H T Nguyen, A W M Smeulders","year":"2004","venue":"Pro-ceedings of the 21st International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Retz and Schwenker, 2016] R. Retz and F. Schwenker. Ac- tive multi-instance multi-label learning. In Proceedings of the 2nd European Conference on Data Analysis, pages 91-101, 2016.","title":"Active multi-instance multi-label learning","authors":"R Retz, F Schwenker","year":"2016","venue":"Proceedings of the 2nd European Conference on Data Analysis","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Salmani and Sridharan, 2014] K. Salmani and M. Sridha- ran. Multi-instance active learning with online labeling for object recognition. In Proceedings of the 27th Interna- tional Flairs Conference, 2014.","title":"Multi-instance active learning with online labeling for object recognition","authors":"K Salmani, M Sridharan","year":"2014","venue":"Proceedings of the 27th International Flairs Conference","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Settles et al., 2007] B. Settles, M. Craven, and S. Ray. Multiple-instance active learning. In Advances in Neu- ral Information Processing Systems 20, pages 1289-1296, 2007.","title":"Multiple-instance active learning","authors":"B Settles, M Craven, S Ray","year":"2007","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Settles, 2009] B. Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.","title":"Active learning literature survey","authors":"B Settles","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Surdeanu et al., 2012] M. Surdeanu, J. Tibshirani, R. Nalla- pati, and C. D. Manning. Multi-instance multi-label learn- ing for relation extraction. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Lan- guage Processing and Computational Natural Language Learning,, pages 455-465, 2012.","title":"Multi-instance multi-label learning for relation extraction","authors":"M Surdeanu, J Tibshirani, R Nallapati, C D Manning","year":"2012","venue":"Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tang et al., 2012] J. Tang, Z.-J. Zha, D. Tao, and T.-S. Chua. Semantic-gap-oriented active learning for multilabel im- age annotation. IEEE Transactions on Image Processing, 21(4):2354-2360, 2012.","title":"Semantic-gap-oriented active learning for multilabel image annotation","authors":"J Tang, Z.-J Zha, D Tao, T.-S Chua","year":"2012","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Winn et al., 2005] J. M. Winn, A. Criminisi, and T. P. Minka. Object categorization by learned universal visual dictionary. In Proceedings of the 10th IEEE International Conference on Computer Vision, pages 1800-1807, 2005.","title":"Object categorization by learned universal visual dictionary","authors":"J M Winn, A Criminisi, T P Minka","year":"2005","venue":"Proceedings of the 10th IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wu et al., 2014] J. Wu, V. Sheng, J. Zhang, P. Zhao, and Z. Cui. Multi-label active learning for image classifica- tion. In Proceedings of IEEE International Conference on Image Processing, pages 5227-5231, 2014.","title":"Multi-label active learning for image classification","authors":"J Wu, V Sheng, J Zhang, P Zhao, Z Cui","year":"2014","venue":"Proceedings of IEEE International Conference on Image Processing","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Xu et al., 2011] X.-S. Xu, X. Xue, and Z.-H. Zhou. Ensem- ble multi-instance multi-label learning approach for video annotation task. In Proceedings of the 19th International Conference on Multimedia, pages 1153-1156, 2011.","title":"Ensemble multi-instance multi-label learning approach for video annotation task","authors":"Xu","year":"2011","venue":"Proceedings of the 19th International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhang and Zhou, 2008] M.-L. Zhang and Z.-H. Zhou. M3MIML: A maximum margin method for multi-instance multi-label learning. In Proceedings of the 8th IEEE In- ternational Conference on Data Mining, pages 688-697, 2008.","title":"M3MIML: A maximum margin method for multi-instance multi-label learning","authors":"M.-L Zhang, Z.-H Zhou","year":"2008","venue":"Proceedings of the 8th IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang et al., 2010] D. Zhang, F. Wang, Z. Shi, and C. Zhang. Interactive localized content based image re- trieval with multiple-instance active learning. Pattern Recognition, 43(2):478-484, 2010.","title":"Interactive localized content based image retrieval with multiple-instance active learning","authors":"D Zhang, F Wang, Z Shi, C Zhang","year":"2010","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhou and Zhang, 2006] Z.-H. Zhou and M.-L. Zhang. Multi-instance multi-label learning with application to scene classification. In Advances in Neural Information Processing Systems 19, pages 1609-1616, 2006.","title":"Multi-instance multi-label learning with application to scene classification","authors":"Z.-H Zhou, M.-L Zhang","year":"2006","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhou et al., 2012] Z.-H. Zhou, M.-L. Zhang, S.-J. Huang, and Y.-F. Li. Multi-instance multi-label learning. Artificial Intelligence, 176(1):2291-2320, 2012.","title":"Multi-instance multi-label learning","authors":"Z.-H Zhou, M.-L Zhang, S.-J Huang, Y.-F Li","year":"2012","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}