{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T20:15:46Z","timestamp":1694117746443},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2018,8,28]],"date-time":"2018-08-28T00:00:00Z","timestamp":1535414400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2019,4]]},"DOI":"10.1007\/s10994-018-5759-4","type":"journal-article","created":{"date-parts":[[2018,8,29]],"date-time":"2018-08-29T20:03:00Z","timestamp":1535572980000},"page":"627-658","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The risk of trivial solutions in bipartite top ranking"],"prefix":"10.1007","volume":"108","author":[{"given":"Aditya Krishna","family":"Menon","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,8,28]]},"reference":[{"key":"5759_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal, S. (2011). The infinite push: A new support vector ranking algorithm that directly optimizes accuracy at the absolute top of the list. In SIAM international conference on data mining (SDM).","DOI":"10.1137\/1.9781611972818.72"},{"key":"5759_CR2","first-page":"393","volume":"6","author":"S Agarwal","year":"2005","unstructured":"Agarwal, S., Graepel, T., Herbrich, R., Har-Peled, S., & Roth, D. (2005). Generalization bounds for the area under the ROC curve. Journal of Machine Learning Research, 6, 393\u2013425.","journal-title":"Journal of Machine Learning Research"},{"key":"5759_CR3","doi-asserted-by":"crossref","unstructured":"Agarwal, S., & Niyogi, P. (2005). Stability and generalization of bipartite ranking algorithms. In Conference on learning theory (COLT).","DOI":"10.1007\/11503415_3"},{"issue":"473","key":"5759_CR4","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1198\/016214505000000907","volume":"101","author":"PL Bartlett","year":"2006","unstructured":"Bartlett, P. L., Jordan, M. I., & Mcauliffe, J. D. (2006). Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101(473), 138\u2013156.","journal-title":"Journal of the American Statistical Association"},{"key":"5759_CR5","unstructured":"Ben-David, S., Loker, D., Srebro, N., & Sridharan, K. (2012). Minimizing the misclassification error rate using a surrogate convex loss. In Langford, J., Pineau, J. (Eds.), Proceedings of the 29th international conference on machine learning (ICML-12), Omnipress, New York, NY, USA, ICML \u201912, (pp. 1863\u20131870)."},{"key":"5759_CR6","unstructured":"Boyd, S. P., Cortes, C., Mohri, M., & Radovanovic, A. (2012). Accuracy at the top. In Advances in neural information processing systems (NIPS)."},{"key":"5759_CR7","doi-asserted-by":"crossref","unstructured":"Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010). The balanced accuracy and its posterior distribution. In International conference on pattern recognition (ICPR).","DOI":"10.1109\/ICPR.2010.764"},{"key":"5759_CR8","unstructured":"Buja, A., Stuetzle, W., & Shen, Y. (2005). Loss functions for binary class probability estimation and classification: Structure and applications. Retrieved from http:\/\/stat.wharton.upenn.edu\/~buja\/PAPERS\/paper-proper-scoring.pdf ."},{"key":"5759_CR9","unstructured":"Chan, P. K., & Stolfo, S. J. (1998). Learning with non-uniform class and cost distributions: Effects and a multi-classifier approach. In KDD 1998 workshop on distributed data mining."},{"issue":"2","key":"5759_CR10","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1214\/009052607000000910","volume":"36","author":"S Cl\u00e9men\u00e7on","year":"2008","unstructured":"Cl\u00e9men\u00e7on, S., Lugosi, G., & Vayatis, N. (2008). Ranking and empirical minimization of U-statistics. The Annals of Statistics, 36(2), 844\u2013874.","journal-title":"The Annals of Statistics"},{"key":"5759_CR11","first-page":"2671","volume":"8","author":"S Cl\u00e9men\u00e7on","year":"2007","unstructured":"Cl\u00e9men\u00e7on, S., & Vayatis, N. (2007). Ranking the best instances. Journal of Machine Learning Research, 8, 2671\u20132699.","journal-title":"Journal of Machine Learning Research"},{"key":"5759_CR12","first-page":"2905","volume":"12","author":"C Ertekin","year":"2011","unstructured":"Ertekin, C., & Rudin, C. (2011). On equivalence relationships between classification and ranking algorithms. Journal of Machine Learning Research, 12, 2905\u20132929.","journal-title":"Journal of Machine Learning Research"},{"key":"5759_CR13","first-page":"933","volume":"4","author":"Y Freund","year":"2003","unstructured":"Freund, Y., Iyer, R., Schapire, R. E., & Singer, Y. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4, 933\u2013969.","journal-title":"Journal of Machine Learning Research"},{"key":"5759_CR14","unstructured":"Gao, W., & Zhou, Z. (2015). On the consistency of AUC pairwise optimization. In International joint conference on artificial intelligence (IJCAI)."},{"key":"5759_CR15","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.neucom.2014.09.081","volume":"160","author":"A Ghosh","year":"2015","unstructured":"Ghosh, A., Manwani, N., & Sastry, P. S. (2015). Making risk minimization tolerant to label noise. Neurocomputing, 160, 93\u2013107.","journal-title":"Neurocomputing"},{"key":"5759_CR16","doi-asserted-by":"crossref","unstructured":"J\u00e4rvelin, K., & Kek\u00e4l\u00e4inen, J. (2000). IR evaluation methods for retrieving highly relevant documents. In ACM conference on research and development in information retrieval, SIGIR \u201900.","DOI":"10.1145\/345508.345545"},{"key":"5759_CR17","doi-asserted-by":"crossref","unstructured":"Joachims, T. (2005). A support vector method for multivariate performance measures. In Proceedings of the 22nd international conference on machine learning (pp. 377\u2013384). ACM, New York, NY, USA, ICML \u201905.","DOI":"10.1145\/1102351.1102399"},{"key":"5759_CR18","unstructured":"Kar, P., Narasimhan, H., & Jain, P. (2015). Surrogate functions for maximizing precision at the top. In International conference on machine learning (ICML)."},{"key":"5759_CR19","unstructured":"Li, N., Jin, R., & Zhou, Z. (2014a). Top rank optimization in linear time. arXiv:1410.1462 ."},{"key":"5759_CR20","unstructured":"Li, N., Jin, R., & Zhou, Z. H. (2014b). Top rank optimization in linear time. In Advances in neural information processing systems."},{"key":"5759_CR21","unstructured":"Liu, L. P., Dietterich, T. G., Li, N., & Zhou, Z. H. (2015). Transductive optimization of top k precision. In International joint conference on artificial intelligence (IJCAI)."},{"issue":"3","key":"5759_CR22","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s10994-009-5165-z","volume":"78","author":"PM Long","year":"2010","unstructured":"Long, P. M., & Servedio, R. A. (2010). Random classification noise defeats all convex potential boosters. Machine Learning, 78(3), 287\u2013304.","journal-title":"Machine Learning"},{"key":"5759_CR23","unstructured":"Menon, A. K., & Ong, C. S. (2016). Linking losses for density ratio and class-probability estimation. In International conference on machine learning (ICML)."},{"key":"5759_CR24","unstructured":"Narasimhan, H., & Agarwal, S. (2013). SVMpAUC: a new support vector method for optimizing partial AUC based on a tight convex upper bound. In ACM international conference on knowledge discovery and data mining (KDD)."},{"key":"5759_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-8853-9","volume-title":"Introductory lectures on convex optimization: A basic course","author":"Y Nesterov","year":"2004","unstructured":"Nesterov, Y. (2004). Introductory lectures on convex optimization: A basic course. Alphen aan den Rijn: Kluwer Academic Publishers."},{"key":"5759_CR26","first-page":"1201","volume-title":"Advances in neural information processing systems 21","author":"R Nock","year":"2009","unstructured":"Nock, R., & Nielsen, F. (2009). On the efficient minimization of classification calibrated surrogates. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bottou (Eds.), Advances in neural information processing systems 21 (pp. 1201\u20131208). Red Hook: Curran Associates, Inc."},{"key":"5759_CR27","unstructured":"Rakotomamonjy, A. (2012). Sparse support vector infinite push. In International conference on machine learning (ICML) (pp. 339\u2013346), Omnipress, USA, ICML\u201912."},{"key":"5759_CR28","first-page":"2387","volume":"11","author":"MD Reid","year":"2010","unstructured":"Reid, M. D., & Williamson, R. C. (2010). Composite binary losses. Journal of Machine Learning Research, 11, 2387\u20132422.","journal-title":"Journal of Machine Learning Research"},{"key":"5759_CR29","first-page":"2233","volume":"10","author":"C Rudin","year":"2009","unstructured":"Rudin, C. (2009). The p-norm push: A simple convex ranking algorithm that concentrates at the top of the list. Journal of Machine Learning Research, 10, 2233\u20132271.","journal-title":"Journal of Machine Learning Research"},{"key":"5759_CR30","unstructured":"Rudin, C., & Wang, Y. (2018). Direct learning to rank and rerank. In A. Storkey, F. Perez-Cruz (Eds.), Proceedings of the twenty-first international conference on artificial intelligence and statistics, PMLR, Playa Blanca, Lanzarote, Canary Islands, Proceedings of Machine Learning Research (Vol.\u00a084, pp. 775\u2013783)."},{"key":"5759_CR31","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1214\/12-EJS699","volume":"6","author":"C Scott","year":"2012","unstructured":"Scott, C. (2012). Calibrated asymmetric surrogate losses. Electronic Journal of Statistics, 6, 958\u2013992.","journal-title":"Electronic Journal of Statistics"},{"key":"5759_CR32","unstructured":"Uematsu, K., & Lee, Y. (2012). On theoretically optimal ranking functions in bipartite ranking. Unpublished manuscript."},{"issue":"3","key":"5759_CR33","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1007\/s10994-011-5268-1","volume":"86","author":"H Xu","year":"2012","unstructured":"Xu, H., & Mannor, S. (2012). Robustness and generalization. Machine Learning, 86(3), 391\u2013423.","journal-title":"Machine Learning"},{"key":"5759_CR34","doi-asserted-by":"crossref","unstructured":"Yue, Y., Finley, T., Radlinski, F., & Joachims, T. (2007). A support vector method for optimizing average precision. In ACM conference on research and development in information retrieval (SIGIR).","DOI":"10.1145\/1277741.1277790"},{"key":"5759_CR35","unstructured":"Yun, H., Raman, P., & Vishwanathan, S. V. N. (2014). Ranking via robust binary classification. In Advances in neural information processing systems (NIPS)."},{"key":"5759_CR36","doi-asserted-by":"publisher","DOI":"10.1142\/5021","volume-title":"Convex analysis in general vector spaces","author":"C Z\u0103linescu","year":"2002","unstructured":"Z\u0103linescu, C. (2002). Convex analysis in general vector spaces. Singapore: World Scientific."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-018-5759-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-018-5759-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-018-5759-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T17:47:14Z","timestamp":1693849634000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-018-5759-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,28]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2019,4]]}},"alternative-id":["5759"],"URL":"https:\/\/doi.org\/10.1007\/s10994-018-5759-4","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,28]]},"assertion":[{"value":"27 January 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}