{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T02:16:42Z","timestamp":1781835402775,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":58,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF of China","award":["61973162, 62006202"],"award-info":[{"award-number":["61973162, 62006202"]}]},{"name":"the Ant Financial Science Funds for Security Research of Ant Financial"},{"name":"HKBU CSD Departmental Incentive Grant"},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30920032202, 30921013114"],"award-info":[{"award-number":["30920032202, 30921013114"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the RGC Early Career Scheme","award":["22200720"],"award-info":[{"award-number":["22200720"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482433","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T15:31:19Z","timestamp":1636990279000},"page":"2497-2506","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Fraud Detection under Multi-Sourced Extremely Noisy Annotations"],"prefix":"10.1145","author":[{"given":"Chuang","family":"Zhang","sequence":"first","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qizhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hongkong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tengfei","family":"Liu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Lu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Hong","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Han","sequence":"additional","affiliation":[{"name":"Hong Kong Baptist University, Hongkong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Gong","sequence":"additional","affiliation":[{"name":"Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-39736-3_17"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2528120"},{"key":"e_1_3_2_1_3_1","volume-title":"Credit card fraud detection using machine learning techniques: A comparative analysis","author":"Awoyemi John O","unstructured":"John O Awoyemi , Adebayo O Adetunmbi , and Samuel A Oluwadare . 2017. Credit card fraud detection using machine learning techniques: A comparative analysis . In ICCNI. IEEE , 1--9. John O Awoyemi, Adebayo O Adetunmbi, and Samuel A Oluwadare. 2017. Credit card fraud detection using machine learning techniques: A comparative analysis. In ICCNI. IEEE, 1--9."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/3042573.3042588"},{"key":"e_1_3_2_1_5_1","volume-title":"Confidence Scores Make Instance-dependent Label-noise Learning Possible. ArXiv Preprint ArXiv:2001.03772","author":"Berthon Antonin","year":"2020","unstructured":"Antonin Berthon , Bo Han , Gang Niu , Tongliang Liu , and Masashi Sugiyama . 2020. Confidence Scores Make Instance-dependent Label-noise Learning Possible. ArXiv Preprint ArXiv:2001.03772 ( 2020 ). Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama. 2020. Confidence Scores Make Instance-dependent Label-noise Learning Possible. ArXiv Preprint ArXiv:2001.03772 (2020)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2010.08.008"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1162264"},{"key":"e_1_3_2_1_8_1","volume-title":"Mariana SC Almeida, Jo ao Tiago Ascens ao, and Pedro Bizarro.","author":"Branco Bernardo","year":"2020","unstructured":"Bernardo Branco , Pedro Abreu , Ana Sofia Gomes , Mariana SC Almeida, Jo ao Tiago Ascens ao, and Pedro Bizarro. 2020 . Interleaved Sequence RNNs for Fraud Detection. In SIGKDD. 3101--3109. Bernardo Branco, Pedro Abreu, Ana Sofia Gomes, Mariana SC Almeida, Jo ao Tiago Ascens ao, and Pedro Bizarro. 2020. Interleaved Sequence RNNs for Fraud Detection. In SIGKDD. 3101--3109."},{"key":"e_1_3_2_1_9_1","volume-title":"Olivier Caelen, and Gianluca Bontempi.","author":"Carcillo Fabrizio","year":"2017","unstructured":"Fabrizio Carcillo , Yann-Ael Le Borgne , Olivier Caelen, and Gianluca Bontempi. 2017 . An assessment of streaming active learning strategies for real-life credit card fraud detection. In IEEE DSAA. IEEE , 631--639. Fabrizio Carcillo, Yann-Ael Le Borgne, Olivier Caelen, and Gianluca Bontempi. 2017. An assessment of streaming active learning strategies for real-life credit card fraud detection. In IEEE DSAA. IEEE, 631--639."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41060-018-0116-z"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2017.01.002"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_13_1","unstructured":"Jiacheng Cheng Tongliang Liu Kotagiri Ramamohanarao and Dacheng Tao. 2020. Learning with bounded instance-and label-dependent label noise. In ICML.  Jiacheng Cheng Tongliang Liu Kotagiri Ramamohanarao and Dacheng Tao. 2020. Learning with bounded instance-and label-dependent label noise. In ICML."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346806"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219878"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/2886521.2886681"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.","author":"Kab\u00e1n Ata","year":"2014","unstructured":"Beno^it Fr\u00e9nay, Ata Kab\u00e1n , 2014 . A comprehensive introduction to label noise . In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Beno^it Fr\u00e9nay, Ata Kab\u00e1n, et al. 2014. A comprehensive introduction to label noise. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning."},{"key":"e_1_3_2_1_18_1","volume-title":"Greedy function approximation: a gradient boosting machine. Annals of Statistics","author":"Friedman Jerome H","year":"2001","unstructured":"Jerome H Friedman . 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics ( 2001 ), 1189--1232. Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of Statistics (2001), 1189--1232."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020460"},{"key":"e_1_3_2_1_20_1","volume-title":"IJITEE","volume":"4","author":"Gaikwad Jyoti R","year":"2014","unstructured":"Jyoti R Gaikwad , Amruta B Deshmane , Harshada V Somavanshi , Snehal V Patil , and Rinku A Badgujar . 2014 . Credit Card Fraud Detection using Decision Tree Induction Algorithm . IJITEE , Vol. 4 , 6 (2014). Jyoti R Gaikwad, Amruta B Deshmane, Harshada V Somavanshi, Snehal V Patil, and Rinku A Badgujar. 2014. Credit Card Fraud Detection using Decision Tree Induction Algorithm. IJITEE, Vol. 4, 6 (2014)."},{"key":"e_1_3_2_1_21_1","volume-title":"Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric environment","author":"Gardner Matt W","year":"1998","unstructured":"Matt W Gardner and SR Dorling . 1998. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric environment , Vol. 32 , 14--15 ( 1998 ), 2627--2636. Matt W Gardner and SR Dorling. 1998. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric environment , Vol. 32, 14--15 (1998), 2627--2636."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298483.3298518"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.09.081"},{"key":"e_1_3_2_1_24_1","unstructured":"Jacob Goldberger and Ehud Ben-Reuven. 2016. Training deep neural-networks using a noise adaptation layer. (2016).  Jacob Goldberger and Ehud Ben-Reuven. 2016. Training deep neural-networks using a noise adaptation layer. (2016)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2941684"},{"key":"e_1_3_2_1_26_1","volume-title":"Centroid estimation with guaranteed efficiency: A general framework for weakly supervised learning","author":"Gong Chen","year":"2020","unstructured":"Chen Gong , Jian Yang , Jane J You , and Masashi Sugiyama . 2020. Centroid estimation with guaranteed efficiency: A general framework for weakly supervised learning . IEEE T-PAMI ( 2020 ). Chen Gong, Jian Yang, Jane J You, and Masashi Sugiyama. 2020. Centroid estimation with guaranteed efficiency: A general framework for weakly supervised learning. IEEE T-PAMI (2020)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5119-5"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1755831"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1587\/transfun.E93.A.787"},{"key":"e_1_3_2_1_30_1","unstructured":"Ashish Khetan Zachary C Lipton and Anima Anandkumar. 2018. Learning from noisy singly-labeled data. In ICLR.  Ashish Khetan Zachary C Lipton and Anima Anandkumar. 2018. Learning from noisy singly-labeled data. In ICLR."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Shikun Li Shiming Ge Yingying Hua Chunhui Zhang Hao Wen Tengfei Liu and Weiqiang Wang. 2020. Coupled-View Deep Classifier Learning from Multiple Noisy Annotators.. In AAAI. 4667--4674.  Shikun Li Shiming Ge Yingying Hua Chunhui Zhang Hao Wen Tengfei Liu and Weiqiang Wang. 2020. Coupled-View Deep Classifier Learning from Multiple Noisy Annotators.. In AAAI. 4667--4674.","DOI":"10.1609\/aaai.v34i04.5898"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Can Liu Qiwei Zhong Xiang Ao Li Sun Wangli Lin Jinghua Feng Qing He and Jiayu Tang. 2020. Fraud Transactions Detection via Behavior Tree with Local Intention Calibration. In SIGKDD. 3035--3043.  Can Liu Qiwei Zhong Xiang Ao Li Sun Wangli Lin Jinghua Feng Qing He and Jiayu Tang. 2020. Fraud Transactions Detection via Behavior Tree with Local Intention Calibration. In SIGKDD. 3035--3043.","DOI":"10.1145\/3394486.3403354"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2456899"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1466-8238.2007.00358.x"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Yijing Luo Bo Han and Chen Gong. 2020. A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery.. In IJCAI. 2605--2611.  Yijing Luo Bo Han and Chen Gong. 2020. A Bi-level Formulation for Label Noise Learning with Spectral Cluster Discovery.. In IJCAI. 2605--2611.","DOI":"10.24963\/ijcai.2020\/361"},{"key":"e_1_3_2_1_36_1","volume-title":"Brendan Van Rooyen, and Nagarajan Natarajan","author":"Menon Aditya Krishna","year":"2016","unstructured":"Aditya Krishna Menon , Brendan Van Rooyen, and Nagarajan Natarajan . 2016 . Learning from binary labels with instance-dependent corruption. ArXiv Preprint ArXiv :1605.00751 (2016). Aditya Krishna Menon, Brendan Van Rooyen, and Nagarajan Natarajan. 2016. Learning from binary labels with instance-dependent corruption. ArXiv Preprint ArXiv:1605.00751 (2016)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2487593"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999611.2999745"},{"key":"e_1_3_2_1_39_1","unstructured":"Curtis G Northcutt Tailin Wu and Isaac L Chuang. 2017. Learning with confident examples: Rank pruning for robust classification with noisy labels. In AAAI.  Curtis G Northcutt Tailin Wu and Isaac L Chuang. 2017. Learning with confident examples: Rank pruning for robust classification with noisy labels. In AAAI."},{"key":"e_1_3_2_1_40_1","volume-title":"Richard Nock, and Lizhen Qu.","author":"Patrini Giorgio","year":"2017","unstructured":"Giorgio Patrini , Alessandro Rozza , Aditya Krishna Menon , Richard Nock, and Lizhen Qu. 2017 . Making deep neural networks robust to label noise: A loss correction approach. In CVPR. 1944--1952. Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. 2017. Making deep neural networks robust to label noise: A loss correction approach. In CVPR. 1944--1952."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014763"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756006.1859894"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.5555\/3044805.3044941"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401965"},{"key":"e_1_3_2_1_45_1","unstructured":"https:\/\/github.com\/dmlc\/xgboost. 2020. XGBoost.  https:\/\/github.com\/dmlc\/xgboost. 2020. XGBoost."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Andreas Veit Neil Alldrin Gal Chechik Ivan Krasin Abhinav Gupta and Serge Belongie. 2017. Learning from noisy large-scale datasets with minimal supervision. In CVPR. 839--847.  Andreas Veit Neil Alldrin Gal Chechik Ivan Krasin Abhinav Gupta and Serge Belongie. 2017. Learning from noisy large-scale datasets with minimal supervision. In CVPR. 839--847.","DOI":"10.1109\/CVPR.2017.696"},{"key":"e_1_3_2_1_47_1","volume-title":"A Semi-supervised Graph Attentive Network for Financial Fraud Detection","author":"Wang Daixin","unstructured":"Daixin Wang , Jianbin Lin , Peng Cui , Quanhui Jia , Zhen Wang , Yanming Fang , Quan Yu , Jun Zhou , Shuang Yang , and Yuan Qi. 2019. A Semi-supervised Graph Attentive Network for Financial Fraud Detection . In ICDM. IEEE , 598--607. Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, and Yuan Qi. 2019. A Semi-supervised Graph Attentive Network for Financial Fraud Detection. In ICDM. IEEE, 598--607."},{"key":"e_1_3_2_1_48_1","volume-title":"Deep structure learning for fraud detection","author":"Wang Haibo","unstructured":"Haibo Wang , Chuan Zhou , Jia Wu , Weizhen Dang , Xingquan Zhu , and Jilong Wang . 2018. Deep structure learning for fraud detection . In ICDM. IEEE , 567--576. Haibo Wang, Chuan Zhou, Jia Wu, Weizhen Dang, Xingquan Zhu, and Jilong Wang. 2018. Deep structure learning for fraud detection. In ICDM. IEEE, 567--576."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Qizhou Wang Bo Han Tongliang Liu Gang Niu Jian Yang and Chen Gong. 2021a. Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. In AAAI. 10183--10191.  Qizhou Wang Bo Han Tongliang Liu Gang Niu Jian Yang and Chen Gong. 2021a. Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model. In AAAI. 10183--10191.","DOI":"10.1609\/aaai.v35i11.17221"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"crossref","unstructured":"Qizhou Wang Jiangchao Yao Chen Gong Tongliang Liu Mingming Gong Hongxia Yang and Bo Han. 2021b. Learning with Group Noise. In AAAI. 10192--10200.  Qizhou Wang Jiangchao Yao Chen Gong Tongliang Liu Mingming Gong Hongxia Yang and Bo Han. 2021b. Learning with Group Noise. In AAAI. 10192--10200.","DOI":"10.1609\/aaai.v35i11.17222"},{"key":"e_1_3_2_1_51_1","first-page":"3178","article-title":"Harnessing side information for classification under label noise","volume":"31","author":"Wei Yang","year":"2019","unstructured":"Yang Wei , Chen Gong , Shuo Chen , Tongliang Liu , Jian Yang , and Dacheng Tao . 2019 . Harnessing side information for classification under label noise . IEEE T-NNLS , Vol. 31 , 9 (2019), 3178 -- 3192 . Yang Wei, Chen Gong, Shuo Chen, Tongliang Liu, Jian Yang, and Dacheng Tao. 2019. Harnessing side information for classification under label noise. IEEE T-NNLS, Vol. 31, 9 (2019), 3178--3192.","journal-title":"IEEE T-NNLS"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/2997046.2997166"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.5555\/2984093.2984321"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3454901"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5412-1"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Chuang Zhang Chen Gong Tengfei Liu Xun Lu Weiqiang Wang and Jian Yang. 2020. Online Positive and Unlabeled Learning.. In IJCAI. 2248--2254.  Chuang Zhang Chen Gong Tengfei Liu Xun Lu Weiqiang Wang and Jian Yang. 2020. Online Positive and Unlabeled Learning.. In IJCAI. 2248--2254.","DOI":"10.24963\/ijcai.2020\/311"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367471.3367632"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219958"}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia","acronym":"CIKM '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482433","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482433","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:23Z","timestamp":1750191143000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482433"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":58,"alternative-id":["10.1145\/3459637.3482433","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482433","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}