{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T15:56:43Z","timestamp":1767801403279,"version":"3.49.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032059611","type":"print"},{"value":"9783032059628","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05962-8_22","type":"book-chapter","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T19:38:09Z","timestamp":1759520289000},"page":"372-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Constrained Optimization to\u00a0Improve Critical Rare Classes Performance Within the\u00a0Top-Ranking Part"],"prefix":"10.1007","author":[{"given":"Yuxin","family":"Ying","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuzhen","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingyuan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daixin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobo","family":"Qin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"107348","DOI":"10.1109\/ACCESS.2023.3320072","volume":"11","author":"FA Almarshad","year":"2023","unstructured":"Almarshad, F.A., Gashgari, G.A., Alzahrani, A.I.A.: Generative adversarial networks-based novel approach for fraud detection for the european cardholders 2013 dataset. IEEE Access 11, 107348\u2013107368 (2023)","journal-title":"IEEE Access"},{"issue":"2","key":"22_CR2","first-page":"133","volume":"12","author":"DP Bertsekas","year":"1976","unstructured":"Bertsekas, D.P.: Multiplier methods: a survey. Autom. 12(2), 133\u2013145 (1976)","journal-title":"Multiplier methods: a survey. Autom."},{"key":"22_CR3","unstructured":"Bhatia, K., et al.: The extreme classification repository: multi-label datasets and code (2016). http:\/\/manikvarma.org\/downloads\/XC\/XMLRepository.html"},{"key":"22_CR4","unstructured":"Cao, K., Wei, C., Gaidon, A., Ar\u00e9chiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: NeurIPS, pp. 1565\u20131576 (2019)"},{"key":"22_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-3-030-65414-6_9","volume-title":"Computer Vision \u2013 ECCV 2020 Workshops","author":"H-P Chou","year":"2020","unstructured":"Chou, H.-P., Chang, S.-C., Pan, J.-Y., Wei, W., Juan, D.-C.: Remix: rebalanced mixup. In: Bartoli, A., Fusiello, A. (eds.) ECCV 2020. LNCS, vol. 12540, pp. 95\u2013110. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-65414-6_9"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T., Song, Y., Belongie, S.J.: Class-balanced loss based on effective number of samples. In: CVPR, pp. 9268\u20139277. Computer Vision Foundation\/IEEE (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"issue":"12","key":"22_CR7","doi-asserted-by":"publisher","first-page":"5561","DOI":"10.1109\/TNNLS.2020.2969527","volume":"31","author":"S Gultekin","year":"2020","unstructured":"Gultekin, S., Saha, A., Ratnaparkhi, A., Paisley, J.W.: Mba: mini-batch auc optimization. IEEE Trans. Neural Networks Learn. Syst. 31(12), 5561\u20135574 (2020)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for CTR prediction, pp. 1725\u20131731 (2017)","DOI":"10.24963\/ijcai.2017\/239"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Loy, C.C., Tang, X.: Learning deep representation for imbalanced classification. In: CVPR, pp. 5375\u20135384. IEEE Computer Society (2016)","DOI":"10.1109\/CVPR.2016.580"},{"key":"22_CR10","doi-asserted-by":"publisher","first-page":"30628","DOI":"10.1109\/ACCESS.2023.3262020","volume":"11","author":"DM Ibomoiye","year":"2023","unstructured":"Ibomoiye, D.M., Sun, Y.: A deep learning ensemble with data resampling for credit card fraud detection. IEEE Access 11, 30628\u201330638 (2023)","journal-title":"IEEE Access"},{"key":"22_CR11","doi-asserted-by":"publisher","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: IR evaluation methods for retrieving highly relevant documents. In: Yannakoudakis, E.J., Belkin, N.J., Ingwersen, P., Leong, M. (eds.) SIGIR 2000: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, July 24-28, 2000, Athens, Greece, pp. 41\u201348. ACM (2000). https:\/\/doi.org\/10.1145\/345508.345545, https:\/\/doi.org\/10.1145\/345508.345545","DOI":"10.1145\/345508.345545"},{"key":"22_CR12","unstructured":"Kumar, A., Narasimhan, H., Cotter, A.: Implicit rate-constrained optimization of non-decomposable objectives. In: ICML. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 5861\u20135871. PMLR (2021)"},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"Li, Z., Kamnitsas, K., Glocker, B.: Overfitting of neural nets under class imbalance: analysis and improvements for segmentation. In: Shen, D., et al., (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 402\u2013410. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_45","DOI":"10.1007\/978-3-030-32248-9_45"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., Girshick, R.B., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: ICCV, pp. 2999\u20133007. IEEE Computer Society (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"22_CR15","unstructured":"Liu, W., Wen, Y., Yu, Z., Yang, M.: Large-margin softmax loss for convolutional neural networks. In: ICML. JMLR Workshop and Conference Proceedings, vol.\u00a048, pp. 507\u2013516. JMLR.org (2016)"},{"issue":"3","key":"22_CR16","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1177\/0272989X8900900307","volume":"9","author":"DK McClish","year":"1989","unstructured":"McClish, D.K.: Analyzing a portion of the roc curve. Med. Decis. Making 9(3), 190\u2013195 (1989)","journal-title":"Med. Decis. Making"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Mohammadi, K., Zhao, H., Zhai, M., Tung, F.: Ranking regularization for critical rare classes: minimizing false positives at a high true positive rate. In: CVPR, pp. 15783\u201315792. IEEE (2023)","DOI":"10.1109\/CVPR52729.2023.01515"},{"key":"22_CR18","unstructured":"Narasimhan, H., Agarwal, S.: A structural SVM based approach for optimizing partial AUC. In: ICML (1). JMLR Workshop and Conference Proceedings, vol.\u00a028, pp. 516\u2013524. JMLR.org (2013)"},{"key":"22_CR19","unstructured":"Sangalli, S., Erdil, E., H\u00f6tker, A.M., Donati, O., Konukoglu, E.: Constrained optimization to train neural networks on critical and under-represented classes. In: NeurIPS, pp. 25400\u201325411 (2021)"},{"key":"22_CR20","unstructured":"Schultheis, E., Kot\u0142owski, W., Wydmuch, M., Babbar, R., Borman, S., Dembczy\u0144ski, K.: Consistent algorithms for multi-label classification with macro-at-k metrics. In: 12th International Conference on Learning Representations (ICLR 2024). Curran Associates Inc., United States (2024)"},{"key":"22_CR21","unstructured":"Schultheis, E., Wydmuch, M., Kotlowski, W., Babbar, R., Dembczynski, K.: Generalized test utilities for long-tail performance in extreme multi-label classification. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems. vol.\u00a036, pp. 22269\u201322303. Curran Associates, Inc. (2023)"},{"key":"22_CR22","unstructured":"Shao, H., Xu, Q., Yang, Z., Bao, S., Huang, Q.: Asymptotically unbiased instance-wise regularized partial AUC optimization: theory and algorithm. In: NeurIPS (2022)"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Thai-Nghe, N., Gantner, Z., Schmidt-Thieme, L.: Cost-sensitive learning methods for imbalanced data. In: IJCNN, pp.\u00a01\u20138. IEEE (2010)","DOI":"10.1109\/IJCNN.2010.5596486"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Wang, S., Liu, W., Wu, J., Cao, L., Meng, Q., Kennedy, P.J.: Training deep neural networks on imbalanced data sets. In: IJCNN, pp. 4368\u20134374. IEEE (2016)","DOI":"10.1109\/IJCNN.2016.7727770"},{"issue":"2","key":"22_CR25","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1093\/biostatistics\/kxq052","volume":"12","author":"Z Wang","year":"2011","unstructured":"Wang, Z., Chang, Y.I.: Marker selection via maximizing the partial area under the roc curve of linear risk scores. Biostatistics 12(2), 369\u201385 (2011)","journal-title":"Biostatistics"},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Wei, J., Wang, S., Huang, Q.: F$${^3}$$net: fusion, feedback and focus for salient object detection. In: AAAI, pp. 12321\u201312328. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i07.6916"},{"key":"22_CR27","unstructured":"Xu, Z., Chai, Z., Yuan, C.: Towards calibrated model for long-tailed visual recognition from prior perspective. In: NeurIPS, pp. 7139\u20137152 (2021)"},{"key":"22_CR28","unstructured":"Yang, Z., Xu, Q., Bao, S., He, Y., Cao, X., Huang, Q.: When all we need is a piece of the pie: a generic framework for optimizing two-way partial AUC. In: ICML. Proceedings of Machine Learning Research, vol.\u00a0139, pp. 11820\u201311829. PMLR (2021)"},{"key":"22_CR29","unstructured":"Yao, Y., Lin, Q., Yang, T.: Large-scale optimization of partial AUC in a range of false positive rates. In: NeurIPS (2022)"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Zadrozny, B., Langford, J., Abe, N.: Cost-sensitive learning by cost-proportionate example weighting. In: ICDM, p.\u00a0435. IEEE Computer Society (2003)","DOI":"10.1109\/ICDM.2003.1250950"},{"key":"22_CR31","unstructured":"Zhang, H., Ciss\u00e9, M., Dauphin, Y.N., Lopez-Paz, D.: Mixup: beyond empirical risk minimization. In: ICLR (Poster). OpenReview.net (2018)"},{"key":"22_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: Pre-trained online contrastive learning for insurance fraud detection. In: AAAI, pp. 22511\u201322519. AAAI Press (2024)","DOI":"10.1609\/aaai.v38i20.30259"},{"key":"22_CR33","unstructured":"Zhu, D., Li, G., Wang, B., Wu, X., Yang, T.: When AUC meets DRO: optimizing partial AUC for deep learning with non-convex convergence guarantee. In: ICML. Proceedings of Machine Learning Research, vol.\u00a0162, pp. 27548\u201327573. PMLR (2022)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05962-8_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T13:06:40Z","timestamp":1767791200000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05962-8_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"ISBN":["9783032059611","9783032059628"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05962-8_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]},"assertion":[{"value":"4 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}