{"type":"ScholarlyArticle","title":"Learning from Few Positives: a Provably Accurate Metric Learning Algorithm to Deal with Imbalanced Data","authors":[{"name":"Rémi Viola","slug":"remi-viola","given":"Rémi","family":"Viola","affiliation":"University of Lyon , UJM-Saint-Etienne , CNRS , Institute of Optics Graduate School , Laboratoire Hubert Curien; DGFiP , Ministry of Economy and Finances , Paris , France"},{"name":"Rémi Emonet","slug":"remi-emonet","given":"Rémi","family":"Emonet","affiliation":"University of Lyon , UJM-Saint-Etienne , CNRS , Institute of Optics Graduate School , Laboratoire Hubert Curien"},{"name":"Amaury Habrard","slug":"amaury-habrard","given":"Amaury","family":"Habrard","affiliation":"University of Lyon , UJM-Saint-Etienne , CNRS , Institute of Optics Graduate School , Laboratoire Hubert Curien"},{"name":"Guillaume Metzler","slug":"guillaume-metzler","given":"Guillaume","family":"Metzler","affiliation":"University of Lyon , UJM-Saint-Etienne , CNRS , Institute of Optics Graduate School , Laboratoire Hubert Curien; UMR 5516 , Saint-Etienne , France"},{"name":"Marc Sebban","slug":"marc-sebban","given":"Marc","family":"Sebban","affiliation":"University of Lyon , UJM-Saint-Etienne , CNRS , Institute of Optics Graduate School , Laboratoire Hubert Curien"}],"abstract":"Learning from imbalanced data, where the positive examples are very scarce, remains a challenging task from both a theoretical and algorithmic perspective. In this paper, we address this problem using a metric learning strategy.  Unlike the state-of-the-art methods, our algorithm MLFP, for Metric Learning from Few Positives, learns a new representation that is used only when a test query is compared to a minority training example. From a geometric perspective, it artificially brings positive examples closer to the query without changing the distances to the negative (majority class) data. This strategy allows us to expand the decision boundaries around the positives, yielding a better F-Measure, a criterion which is suited to deal with imbalanced scenarios. Beyond the algorithmic contribution provided by MLFP, our paper presents generalization guarantees on the false positive and false negative rates. Extensive experiments conducted on several imbalanced datasets show the effectiveness of our method.","doi":"10.24963/ijcai.2020/298","url":"https://ijcai.org/proceedings/2020/298","pdf":"https://ijcai.org/proceedings/2020/0298.pdf","markdown":"https://ijcai.org/proceedings/2020/298.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/298","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":298,"pages":{"first":2155,"last":2161},"keywords":["Machine Learning Applications: Applications of Supervised Learning","Machine Learning: Classification"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24487/file_24487.m3u8","length_sec":282,"provider":"hls"}],"references":[{"position":1,"raw":"[Abdallah et al., 2016] Aisha Abdallah, Mohd Aizaini Maarof, and Anazida Zainal. Fraud detection system: A survey. Journal of Network and Computer Applications, 68:90-113, 2016.","title":"Fraud detection system: A survey","authors":"Aisha Abdallah, Mohd Aizaini Maarof, Anazida Zainal","year":"2016","venue":"Journal of Network and Computer Applications","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Aggarwal, 2017] Charu C. Aggarwal. Outlier Analysis. Springer International Publishing, 2017.","title":"Outlier Analysis","authors":"C Charu, Aggarwal","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bellet et al., 2013] Aurélien Bellet, Amaury Habrard, and Marc Sebban. A survey on metric learning for feature vec- tors and structured data. CoRR, abs/1306.6709, 2013.","title":"A survey on metric learning for feature vectors and structured data","authors":"Aurélien Bellet, Amaury Habrard, Marc Sebban","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bellet et al., 2015] Aurélien Bellet, Amaury Habrard, and Marc Sebban. Metric Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2015.","title":"Metric Learning","authors":"Aurélien Bellet, Amaury Habrard, Marc Sebban","year":"2015","venue":"Synthesis Lectures on Artificial Intelligence and Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bousquet and Elisseeff, 2002] Olivier Bousquet and André Elisseeff. Stability and generalization. Journal of machine learning research, 2(Mar):499-526, 2002.","title":"Stability and generalization","authors":"Elisseeff Bousquet, Olivier Bousquet, André Elisseeff","year":"2002","venue":"Journal of machine learning research","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Davis et al., 2007] Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit Sra, and Inderjit S. Dhillon. Information- theoretic metric learning. In Proceedings of the 24th inter- national conference on Machine learning, pages 209-216. ACM, 2007.","title":"Informationtheoretic metric learning","authors":"Jason V Davis, Brian Kulis, Prateek Jain, Suvrit Sra, Inderjit S Dhillon","year":"2007","venue":"Proceedings of the 24th international conference on Machine learning","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ding et al., 2018] Shuya Ding, Bilal Mirza, Zhiping Lin, Ji- uwen Cao, Xiaoping Lai, Tam V. Nguyen, and Jose Sepul- veda. Kernel based online learning for imbalance multi- class classification. Neurocomputing, 277:139-148, 2018.","title":"Kernel based online learning for imbalance multiclass classification","authors":"Shuya Ding, Bilal Mirza, Zhiping Lin, Jiuwen Cao, Xiaoping Lai, Tam V Nguyen, Jose Sepulveda","year":"2018","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Dumpala et al., 2018] Sri Harsha Dumpala, Rupayan Chakraborty, and Sunil Kumar Kopparapu. A novel data representation for effective learning in class imbalanced scenarios. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, pages 2100-2106, 7 2018.","title":"Sri Harsha Dumpala, Rupayan Chakraborty, and Sunil Kumar Kopparapu. A novel data representation for effective learning in class imbalanced scenarios","authors":"Dumpala","year":"2018","venue":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Feng et al., 2018] Lin Feng, Huibing Wang, Bo Jin, Hao- hao Li, Mingliang Xue, and Le Wang. Learning a dis- tance metric by balancing kl-divergence for imbalanced datasets. IEEE Transactions on Systems, Man, and Cy- bernetics: Systems, PP:1-12, 01 2018.","title":"Learning a distance metric by balancing kl-divergence for imbalanced datasets","authors":"Lin Feng, Huibing Wang, Bo Jin, Haohao Li, Mingliang Xue, Le Wang","year":"2018","venue":"IEEE Transactions on Systems, Man, and Cybernetics: Systems","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Frery et al., 2018] Jordan Frery, Amaury Habrard, Marc Sebban, and Liyun He-Guelton. Non-linear gradient boosting for class-imbalance learning. In 2nd Interna- tional Workshop on Learning with Imbalanced Domains: Theory and Applications, pages 38-51, 2018.","title":"Non-linear gradient boosting for class-imbalance learning","authors":"Frery","year":"2018","venue":"2nd International Workshop on Learning with Imbalanced Domains: Theory and Applications","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gautheron et al., 2019] Léo Gautheron, Emilie Morvant, Amaury Habrard, and Marc Sebban. Metric learning from imbalanced data. In arXiv. 1909.01651, 2019.","title":"Metric learning from imbalanced data","authors":"Léo Gautheron, Emilie Morvant, Amaury Habrard, Marc Sebban","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Huang et al., 2016] Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang. Learning deep rep- resentation for imbalanced classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5375-5384, 2016.","title":"Learning deep representation for imbalanced classification","authors":"Chen Huang, Yining Li, Chen Change Loy, Xiaoou Tang","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Khan et al., 2017] Salman H. Khan, Munawar Hayat, Mo- hammed Bennamoun, Ferdous A. Sohel, and Roberto Togneri. Cost-sensitive learning of deep feature represen- tations from imbalanced data. IEEE transactions on neural networks and learning systems, 29(8):3573-3587, 2017.","title":"Cost-sensitive learning of deep feature representations from imbalanced data","authors":"Salman H Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A Sohel, Roberto Togneri","year":"2017","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kulis and others, 2013] Brian Kulis et al. Metric learning: A survey. Foundations and Trends R in Machine Learn- ing, 5(4):287-364, 2013.","title":"Metric learning: A survey","authors":"Brian Kulis, Kulis","year":"2013","venue":"Foundations and Trends R in Machine Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mathew et al., 2015] Josey Mathew, Ming Luo, Chee Khi- ang Pang, and Hian Leng Chan. Kernel-based smote for svm classification of imbalanced datasets. In IECON 2015-41st Annual Conference of the IEEE Industrial Elec- tronics Society, pages 001127-001132. IEEE, 2015.","title":"Kernel-based smote for svm classification of imbalanced datasets","authors":"Josey Mathew, Ming Luo, Chee Khiang Pang, Hian Leng, Chan","year":"2015","venue":"IECON 2015-41st Annual Conference of the IEEE Industrial Electronics Society","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Pérez-Ortiz et al., 2019] María Pérez-Ortiz, Peter Tiño, Rafal Mantiuk, and César Hervás-Martínez. Exploit- ing synthetically generated data with semi-supervised learning for small and imbalanced datasets. CoRR, abs/1903.10022, 2019.","title":"Exploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets","authors":"Pérez-Ortiz","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Rijsbergen, 1979] Cornelis J. Van Rijsbergen. Information Retrieval. 1979.","title":"Information Retrieval","authors":"J Cornelis, Van Rijsbergen","year":"1979","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Schiller, 2006] Jörg Schiller. The impact of insurance fraud detection systems. Journal of Risk and Insurance, 73(3):421-438, 2006.","title":"The impact of insurance fraud detection systems","authors":"Jörg Schiller, Schiller","year":"2006","venue":"Journal of Risk and Insurance","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Sharma et al., 2018] Shiven Sharma, Colin Bellinger, Bar- tosz Krawczyk, Osmar R. Zaïane, and Nathalie Japkow- icz. Synthetic oversampling with the majority class: A new perspective on handling extreme imbalance. IEEE In- ternational Conference on Data Mining, pages 447-456, 2018.","title":"Synthetic oversampling with the majority class: A new perspective on handling extreme imbalance","authors":"Shiven Sharma, Colin Bellinger, Bartosz Krawczyk, R Osmar, Nathalie Zaïane, Japkowicz","year":"2018","venue":"IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2018] Nan Wang, Xibin Zhao, Yu Jiang, Yue Gao, and KLISS BNRist. Iterative metric learning for imbalance data classification. In 27th International Joint Conference on Artificial Intelligence, pages 2805-2811, 2018.","title":"Iterative metric learning for imbalance data classification","authors":"Nan Wang, Xibin Zhao, Yu Jiang, Yue Gao, Bnrist","year":"2018","venue":"27th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/389"},{"position":21,"raw":"[Weinberger and Saul, 2009] Kilian Q. Weinberger and Lawrence K. Saul. Distance metric learning for large mar- gin nearest neighbor classification. Journal of Machine Learning Research, 10(Feb):207-244, 2009.","title":"Distance metric learning for large margin nearest neighbor classification","authors":"Q Kilian, Lawrence K Weinberger, Saul","year":"2009","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wu et al., 2017] Fei Wu, Xiao-Yuan Jing, Shiguang Shan, Wangmeng Zuo, and Jing-Yu Yang. Multiset feature learn- ing for highly imbalanced data classification. In Proceed- ings of the Thirty-First AAAI Conference on Artificial In- telligence, AAAI'17, page 1583-1589. AAAI Press, 2017.","title":"Multiset feature learning for highly imbalanced data classification","authors":"Fei Wu, Xiao-Yuan Jing, Shiguang Shan, Wangmeng Zuo, Jing-Yu Yang","year":"2017","venue":"Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI'17","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zantedeschi et al., 2016] Valentina Zantedeschi, Rémi Emonet, and Marc Sebban. Metric learning as convex combinations of local models with generalization guaran- tees. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1478-1486, 2016.","title":"Metric learning as convex combinations of local models with generalization guarantees","authors":"Valentina Zantedeschi, Rémi Emonet, Marc Sebban","year":"2016","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang et al., 2019] Xiaogang Zhang, Dingxiang Wang, Yi- cong Zhou, Hua Chen, Fanyong Cheng, and Min Liu. Ker- nel modified optimal margin distribution machine for im- balanced data classification. Pattern Recognition Letters, 125:325-332, 2019.","title":"Kernel modified optimal margin distribution machine for imbalanced data classification","authors":"Xiaogang Zhang, Dingxiang Wang, Yicong Zhou, Hua Chen, Fanyong Cheng, Min Liu","year":"2019","venue":"Pattern Recognition Letters","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}