{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T08:51:30Z","timestamp":1775292690883,"version":"3.50.1"},"reference-count":48,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,12,2]],"date-time":"2018-12-02T00:00:00Z","timestamp":1543708800000},"content-version":"vor","delay-in-days":335,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672033"],"award-info":[{"award-number":["61672033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502004"],"award-info":[{"award-number":["61502004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502001"],"award-info":[{"award-number":["61502001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502012"],"award-info":[{"award-number":["61502012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["1708085MF166"],"award-info":[{"award-number":["1708085MF166"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["18YJC870004"],"award-info":[{"award-number":["18YJC870004"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>Learning to rank has attracted increasing interest in the past decade, due to its wide applications in the areas like document retrieval and collaborative filtering. Feature selection for learning to rank is to select a small number of features from the original large set of features which can ensure a high ranking accuracy, since in many real ranking applications many features are redundant or even irrelevant. To this end, in this paper, a multiobjective evolutionary algorithm, termed MOFSRank, is proposed for feature selection in learning to rank which consists of three components. First, an instance selection strategy is suggested to choose the informative instances from the ranking training set, by which the redundant data is removed and the training efficiency is enhanced. Then on the selected instance subsets, a multiobjective feature selection algorithm with an adaptive mutation is developed, where good feature subsets are obtained by selecting the features with high ranking accuracy and low redundancy. Finally, an ensemble strategy is also designed in MOFSRank, which utilizes these obtained feature subsets to produce a set of better features. Experimental results on benchmark data sets confirm the advantage of the proposed method in comparison with the state\u2010of\u2010the\u2010arts.<\/jats:p>","DOI":"10.1155\/2018\/7837696","type":"journal-article","created":{"date-parts":[[2018,12,2]],"date-time":"2018-12-02T18:35:29Z","timestamp":1543775729000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["MOFSRank: A Multiobjective Evolutionary Algorithm for Feature Selection in Learning to Rank"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0175-0818","authenticated-orcid":false,"given":"Fan","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4946-2607","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5052-000X","authenticated-orcid":false,"given":"Xingyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2018,12,2]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1561\/1500000016"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2556270"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12062"},{"key":"e_1_2_9_4_2","doi-asserted-by":"crossref","unstructured":"CossockD.andZhangT. Subset ranking using regression Proceedings of the Conference on Learning Theory 2006 605\u2013619 https:\/\/doi.org\/10.1007\/11776420_44 MR2280634.","DOI":"10.1007\/11776420_44"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"JoachimsT. Optimizing search engines using clickthrough data Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining July 2002 133\u2013142 2-s2.0-0242456822.","DOI":"10.1145\/775047.775067"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1162\/jmlr.2003.4.6.933"},{"key":"e_1_2_9_7_2","doi-asserted-by":"crossref","unstructured":"BurgesC. ShakedT. RenshawE. LazierA. DeedsM. HamiltonN. andHullenderG. Learning to rank using gradient descent Proceedings of the 22nd International Conference on Machine Learning (ICML \u203205) August 2005 ACM 89\u201396 https:\/\/doi.org\/10.1145\/1102351.1102363 2-s2.0-31844446958.","DOI":"10.1145\/1102351.1102363"},{"key":"e_1_2_9_8_2","doi-asserted-by":"crossref","unstructured":"CaoZ. QinT. LiuT.-Y. TsaiM.-F. andLiH. Learning to rank: from pairwise approach to listwise approach Proceedings of the 24th International Conference on Machine Learning (ICML \u203207) June 2007 Corvallis Ore USA ACM 129\u2013136 https:\/\/doi.org\/10.1145\/1273496.1273513 2-s2.0-34547987951.","DOI":"10.1145\/1273496.1273513"},{"key":"e_1_2_9_9_2","doi-asserted-by":"crossref","unstructured":"XiaF. LiuT.-Y. WangJ. ZhangW. andLiH. Listwise approach to learning to rank - Theory and algorithm Proceedings of the International Conference on Machine Learning 2008 1192\u20131199 2-s2.0-56449094442.","DOI":"10.1145\/1390156.1390306"},{"key":"e_1_2_9_10_2","doi-asserted-by":"crossref","unstructured":"YueY. FinleyT. RadlinskiF. andJoachimsT. A support vector method for optimizing average precision Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval 2007 271\u2013278 https:\/\/doi.org\/10.1145\/1277741.1277790 2-s2.0-36448983903.","DOI":"10.1145\/1277741.1277790"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"XuJ.andLiH. AdaRank: a boosting algorithm for information retrieval Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval 2007 391\u2013398 https:\/\/doi.org\/10.1145\/1277741.1277809 2-s2.0-36448954244.","DOI":"10.1145\/1277741.1277809"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5198-3"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2336697"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt474"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"GengX. LiuT.-Y. QinT. andLiH. Feature selection for ranking Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval 2007 407\u2013414 2-s2.0-36448949756.","DOI":"10.1145\/1277741.1277811"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"HuaG. ZhangM. LiuY. MaS. andRuL. Hierarchical feature selection for ranking Proceedings of the International Conference on World Wide Web WWW 2010 2010 Raleigh North Carolina USA 1113\u20131114 https:\/\/doi.org\/10.1145\/1772690.1772830.","DOI":"10.1145\/1772690.1772830"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"NainiK. D.andAltingovdeI. S. Exploiting Result Diversification Methods for Feature Selection in Learning to Rank Proceedings of the European Conference on Information Retrieval 2014 455\u2013461 https:\/\/doi.org\/10.1007\/978-3-319-06028-6_41.","DOI":"10.1007\/978-3-319-06028-6_41"},{"key":"e_1_2_9_18_2","doi-asserted-by":"crossref","unstructured":"ShirzadM. B.andKeyvanpourM. R. A feature selection method based on minimum redundancy maximum relevance for learning to rank Proceedings of the Ai & Robotics 2015 1\u20135 2-s2.0-84960923335.","DOI":"10.1109\/RIOS.2015.7270735"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"GigliA. LuccheseC. NardiniF. M. andPeregoR. Fast feature selection for learning to rank Proceedings of the International Conference on the Theory of Information Retrieval 2016 167\u2013170 2-s2.0-84991049156.","DOI":"10.1145\/2970398.2970433"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"PanF. ConverseT. AhnD. SalvettiF. andDonatoG. Feature selection for ranking using boosted trees Proceedings of the ACM Conference on Information and Knowledge Management 2009 2025\u20132028 2-s2.0-74549171478.","DOI":"10.1145\/1645953.1646292"},{"key":"e_1_2_9_21_2","doi-asserted-by":"crossref","unstructured":"YuH. OhJ. andHanW. Efficient feature weighting methods for ranking Proceedings of the ACM Conference on Information and Knowledge Management 2009 1157\u20131166 https:\/\/doi.org\/10.1145\/1645953.1646100.","DOI":"10.1145\/1645953.1646100"},{"key":"e_1_2_9_22_2","doi-asserted-by":"crossref","unstructured":"DangV.andCroftB. Feature selection for document ranking using best first search and coordinate ascent Proceedings of the SIGIR Workshop on Feature Generation and Selection for Information Retrieval 2010 1\u20135 https:\/\/doi.org\/10.1016\/j.gene.2010.07.008 2-s2.0-77956880216.","DOI":"10.1016\/j.gene.2010.07.008"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"PahikkalaT. AirolaA. NaulaP. andSalakoskiT. Greedy rankrls: a linear time algorithm for learning sparse ranking models Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval 2010 11\u201318 https:\/\/doi.org\/10.1109\/ICDMW.2011.91.","DOI":"10.1109\/ICDMW.2011.91"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"SunZ. QinT. TaoQ. andWangJ. Robust sparse rank learning for non-smooth ranking measures Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval 2009 259\u2013266 2-s2.0-72449180896.","DOI":"10.1145\/1571941.1571987"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2012.62"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2013.2247628"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2013.2286696"},{"key":"e_1_2_9_28_2","unstructured":"LiP. BurgesC. J. C. andWuQ. Mcrank: learning to rank using multiple classification and gradient boosting Proceedings of the International Conference on Neural Information Processing Systems 2007 897\u2013904."},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.4018\/IJIRR.2018070104"},{"key":"e_1_2_9_30_2","unstructured":"HanX.andLeiS. Feature selection and model comparison on microsoft learning-to-rank data sets https:\/\/arxiv.org\/abs\/1803.05127 2018."},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1002\/asi.22789"},{"key":"e_1_2_9_32_2","doi-asserted-by":"crossref","unstructured":"SousaD. X. CanutoS. D. RosaT. C. MartinsW. S. andGon\u00e7alvesM. A. Incorporating Risk-Sensitiveness into Feature Selection for Learning to Rank Proceedings of the 25th ACM International Conference on Information and Knowledge Management 2016 ACM 257\u2013266 https:\/\/doi.org\/10.1145\/2983323.2983792.","DOI":"10.1145\/2983323.2983792"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.07.043"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.02.031"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/3241489"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.02.022"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-016-2125-y"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2016.2600642"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2017.2708578"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-009-9123-y"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10791-009-9109-9"},{"key":"e_1_2_9_43_2","doi-asserted-by":"crossref","unstructured":"JoachimsT. Training linear SVMs in linear time Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006 217\u2013226 2-s2.0-33749563073.","DOI":"10.1145\/1150402.1150429"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/582415.582418"},{"key":"e_1_2_9_45_2","first-page":"26","article-title":"Modern information retrieval","volume":"43","author":"Ricardo B. Y.","year":"1999","journal-title":"ACM"},{"key":"e_1_2_9_46_2","doi-asserted-by":"crossref","unstructured":"NguyenH. B. XueB. IshibuchiH. AndreaeP. andZhangM. Multiple reference points MOEA\/D for feature selection Proceedings of theGenetic and Evolutionary Computation Conference Companion 2017 Berlin Germany 157\u2013158 https:\/\/doi.org\/10.1145\/3067695.3075985.","DOI":"10.1145\/3067695.3075985"},{"key":"e_1_2_9_47_2","first-page":"95","volume-title":"Evolutionary Methods for Design, Optimization, and Control","author":"Ziztler E.","year":"2002"},{"key":"e_1_2_9_48_2","article-title":"An Indicator Based Multi-Objective Evolutionary Algorithm with Reference Point Adaptation for Better Versatility","author":"Tian Y.","year":"2017","journal-title":"IEEE Transactions on Evolutionary Computation"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/7837696.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/7837696.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2018\/7837696","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T07:58:42Z","timestamp":1775289522000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2018\/7837696"}},"subtitle":[],"editor":[{"given":"Rongqing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2018,1]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,1]]}},"alternative-id":["10.1155\/2018\/7837696"],"URL":"https:\/\/doi.org\/10.1155\/2018\/7837696","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1]]},"assertion":[{"value":"2018-05-28","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-11-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-12-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7837696"}}