{"type":"ScholarlyArticle","title":"Multi-Instance Learning with Key Instance Shift","authors":[{"name":"Ya-Lin Zhang","slug":"ya-lin-zhang","given":"Ya-Lin","family":"Zhang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023 , China"},{"name":"Zhi-Hua Zhou","slug":"zhi-hua-zhou","given":"Zhi-Hua","family":"Zhou","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023 , China"}],"abstract":"Multi-instance learning (MIL) deals with the tasks where each example is represented by a bag of instances. A bag is positive if it contains at least one positive instance, and negative otherwise. The positive instances are also called key instances. Only bag labels are observed, whereas specific instance labels are not available in MIL. Previous studies typically assume that training and test data follow the same distribution, which may be violated in many real-world tasks. In this paper, we address the problem that the distribution of key instances varies between training and test phase. We refer to this problem as MIL with key instance shift and solve it by proposing an embedding based method MIKI. Specifically, to transform the bags into informative vectors, we propose a weighted multi-class model to select the instances with high positiveness as instance prototypes. Then we learn the importance weights for transformed bag vectors and incorporate original instance weights into them to narrow the gap between training/test distributions. Experimental results validate the effectiveness of our approach when key instance shift occurs.","doi":"10.24963/ijcai.2017/481","url":"https://ijcai.org/proceedings/2017/481","pdf":"https://ijcai.org/proceedings/2017/0481.pdf","markdown":"https://ijcai.org/proceedings/2017/481.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/481","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 S-Z","track":"Main track","number":481,"pages":{"first":3441,"last":3447},"keywords":["Machine Learning: Multi-instance/Multi-label/Multi-view learning","Machine Learning: New Problems"],"videos":[],"references":[{"position":1,"raw":"[Amores, 2015] Jaume Amores. MILDE: multiple instance learning by discriminative embedding. Knowledge and Information Systems, 42(2):381-407, 2015.","title":"MILDE: multiple instance learning by discriminative embedding","authors":"Jaume Amores, Amores","year":"2015","venue":"Knowledge and Information Systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Andrews et al., 2002] Stuart Andrews, Ioannis Tsochan- taridis, and Thomas Hofmann. Support vector machines for multiple-instance learning. In Advances in Neural In- formation Processing Systems 15, pages 561-568, 2002.","title":"Support vector machines for multiple-instance learning","authors":"Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann","year":"2002","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Blei et al., 2003] David M Blei, Andrew Y Ng, and Michael I Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.","title":"Latent dirichlet allocation","authors":"Andrew Y David M Blei, Michael I Ng, Jordan","year":"2003","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2006] Yixin Chen, Jinbo Bi, and James Ze Wang. MILES: Multiple-instance learning via embedded instance selection. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 28(12):1931-1947, 2006.","title":"MILES: Multiple-instance learning via embedded instance selection","authors":"Yixin Chen, Jinbo Bi, James Ze, Wang","year":"2006","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dietterich et al., 1997] Thomas G Dietterich, Richard H Lathrop, and Tomás Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1-2):31-71, 1997.","title":"Solving the multiple instance problem with axis-parallel rectangles","authors":"Richard H Thomas G Dietterich, Tomás Lathrop, Lozano-Pérez","year":"1997","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Faria et al., 2017] AWC Faria, FGF Coelho, AM Silva, HP Rocha, GM Almeida, AP Lemos, and AP Braga. MILKDE: A new approach for multiple instance learning based on positive instance selection and kernel density esti- mation. Engineering Applications of Artificial Intelligence, 59:196-204, 2017.","title":"MILKDE: A new approach for multiple instance learning based on positive instance selection and kernel density estimation","authors":"Faria","year":"2017","venue":"Engineering Applications of Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ferrer et al., 2010] Geraldo Ferrer, Nicholas Dew, and Uday Apte. When is RFID right for your service ? International Journal of Production Economics, 124(2):414-425, 2010.","title":"When is RFID right for your service","authors":"Geraldo Ferrer, Nicholas Dew, Uday Apte","year":"2010","venue":"International Journal of Production Economics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gärtner et al., 2002] Thomas Gärtner, Peter A Flach, Adam Kowalczyk, and Alexander J Smola. Multi-instance kernels. In Proceeding of the 19th International Conference on Machine Learning, volume 2, pages 179-186, 2002.","title":"Multi-instance kernels","authors":"Thomas Gärtner, Peter A Flach, Adam Kowalczyk, Alexander J Smola","year":"2002","venue":"Proceeding of the 19th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Herrera et al., 2016] Francisco Herrera, Sebastián Ventura, Rafael Bello, Chris Cornelis, Amelia Zafra, Dánel Sánchez- Tarragó, and Sarah Vluymans. Multiple Instance Learning: Foundations and Algorithms. Springer, 2016.","title":"Multiple Instance Learning: Foundations and Algorithms","authors":"Herrera","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2006] Jiayuan Huang, Arthur Gretton, Karsten M Borgwardt, Bernhard Schölkopf, and Alex J Smola. Correcting sample selection bias by unlabeled data. In Advances in Neural Information Processing Systems 19, pages 601-608, 2006.","title":"Correcting sample selection bias by unlabeled data","authors":"Huang","year":"2006","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kanamori et al., 2009] Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama. A least-squares approach to di- rect importance estimation. Journal of Machine Learning Research, 10:1391-1445, 2009.","title":"A least-squares approach to direct importance estimation","authors":"Takafumi Kanamori, Shohei Hido, Masashi Sugiyama","year":"2009","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2009] Yu-Feng Li, James T Kwok, Ivor W Tsang, and Zhi-Hua Zhou. A convex method for locating regions of interest with multi-instance learning. In Proceeding ot 20th European Conference on Machine Learning, pages 17-32, 2009.","title":"A convex method for locating regions of interest with multi-instance learning","authors":"Yu-Feng Li, James T Kwok, Ivor W Tsang, Zhi-Hua Zhou","year":"2009","venue":"Proceeding ot 20th European Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Maron and Lozano-Pérez, 1998] Oded Maron and Tomás Lozano-Pérez. A framework for multiple-instance learning. In Advances in Neural Information Processing Systems 10, pages 570-576, 1998.","title":"A framework for multiple-instance learning","authors":"Lozano-Pérez ; Maron, Oded Maron, Tomás Lozano-Pérez","year":"1998","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Senta et al., 2007] Yosuke Senta, Yoshihiko Kimuro, Syuhei Takarabe, and Tsutomu Hasegawa. Machine learning ap- proach to self-localization of mobile robots using RFID tag. In Proceedings of the IEEE/ASME International Con- ference on Advanced Intelligent Mechatronics, pages 1-6, 2007.","title":"Machine learning approach to self-localization of mobile robots using RFID tag","authors":"Senta","year":"2007","venue":"Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Shimodaira, 2000] Hidetoshi Shimodaira. Improving pre- dictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2):227-244, 2000.","title":"Improving predictive inference under covariate shift by weighting the log-likelihood function","authors":"Hidetoshi Shimodaira","year":"2000","venue":"Journal of Statistical Planning and Inference","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Sugiyama et al., 2008] Masashi Sugiyama, Shinichi Naka- jima, Hisashi Kashima, Paul V Buenau, and Motoaki Kawanabe. Direct importance estimation with model se- lection and its application to covariate shift adaptation. In Advances in Neural Information Processing Systems 21, pages 1433-1440, 2008.","title":"Direct importance estimation with model selection and its application to covariate shift adaptation","authors":"Sugiyama","year":"2008","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Sugiyama et al., 2012] Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori. Density Ratio Estimation in Machine Learning. Cambridge University Press, 2012.","title":"Density Ratio Estimation in Machine Learning","authors":"Sugiyama","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Wei et al., 2017] Xiu-Shen Wei, Jianxin Wu, and Zhi-Hua Zhou. Scalable algorithms for multi-instance learning. IEEE Transactions on Neural Networks and Learning Sys- tems, 28(4):975-987, 2017.","title":"Scalable algorithms for multi-instance learning","authors":"Xiu-Shen Wei, Jianxin Wu, Zhi-Hua Zhou","year":"2017","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Yuan et al., 2016] Jiazheng Yuan, Xiankai Huang, Hongzhe Liu, Bing Li, and Weihua Xiong. Submil: Discriminative subspaces for multi-instance learning. Neurocomputing, 173:1768-1774, 2016.","title":"Submil: Discriminative subspaces for multi-instance learning","authors":"Yuan","year":"2016","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Zadrozny, 2004] Bianca Zadrozny. Learning and evaluating classifiers under sample selection bias. In Proceeding of the 21st International Conference on Machine Learning, pages 114-121, 2004.","title":"Learning and evaluating classifiers under sample selection bias","authors":"Bianca Zadrozny","year":"2004","venue":"Proceeding of the 21st International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhang and Zhou, 2014] Wei-Jia Zhang and Zhi-Hua Zhou. Multi-instance learning with distribution change. In Pro- ceeding of the 28th AAAI Conference on Artificial Intelli- gence, pages 2184-2190, 2014.","title":"Multi-instance learning with distribution change","authors":"Wei-Jia Zhang, Zhi-Hua Zhou","year":"2014","venue":"Proceeding of the 28th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhang et al., 2002] Qi Zhang, Sally A Goldman, Wei Yu, and Jason E Fritts. Content-based image retrieval using multiple-instance learning. In Proceeding of the 19th Inter- national Conference on Machine Learning, pages 682-689, 2002.","title":"Content-based image retrieval using multiple-instance learning","authors":"Qi Zhang, Sally A Goldman, Wei Yu, Jason E Fritts","year":"2002","venue":"Proceeding of the 19th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhou et al., 2005] Zhi-Hua Zhou, Kai Jiang, and Ming Li. Multi-instance learning based web mining. Applied Intelli- gence, 22(2):135-147, 2005.","title":"Multi-instance learning based web mining","authors":"Zhi-Hua Zhou, Kai Jiang, Ming Li","year":"2005","venue":"Applied Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhou et al., 2009] Zhi-Hua Zhou, Yu-Yin Sun, and Yu-Feng Li. Multi-instance learning by treating instances as non-iid samples. In Proceeding of the 26th International Confer- ence on Machine Learning, pages 1249-1256, 2009.","title":"Multi-instance learning by treating instances as non-iid samples","authors":"Zhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li","year":"2009","venue":"Proceeding of the 26th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}