{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T18:46:23Z","timestamp":1649097983008},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T00:00:00Z","timestamp":1580342400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T00:00:00Z","timestamp":1580342400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"MINECO (the Spanish Ministerio de Econom\u00eda y Competitividad) and FEDER","award":["TIN2015-65069-C2-2-R"],"award-info":[{"award-number":["TIN2015-65069-C2-2-R"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2020,7]]},"DOI":"10.1007\/s10115-020-01436-5","type":"journal-article","created":{"date-parts":[[2020,1,30]],"date-time":"2020-01-30T12:02:46Z","timestamp":1580385766000},"page":"2709-2738","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the $$\\epsilon $$-approximate algorithm for Probabilistic Classifier Chains"],"prefix":"10.1007","volume":"62","author":[{"given":"Miriam","family":"Fdez-D\u00edaz","sequence":"first","affiliation":[]},{"given":"Laura","family":"Fdez-D\u00edaz","sequence":"additional","affiliation":[]},{"given":"Deiner","family":"Mena","sequence":"additional","affiliation":[]},{"given":"Elena","family":"Monta\u00f1\u00e9s","sequence":"additional","affiliation":[]},{"given":"Jos\u00e9 Ram\u00f3n","family":"Quevedo","sequence":"additional","affiliation":[]},{"given":"Juan Jos\u00e9 del","family":"Coz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,30]]},"reference":[{"issue":"1","key":"1436_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","volume":"78","author":"GW Brier","year":"1950","unstructured":"Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78(1):1\u20133","journal-title":"Mon Weather Rev"},{"issue":"2\u20133","key":"1436_CR2","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s10994-009-5127-5","volume":"76","author":"W Cheng","year":"2009","unstructured":"Cheng W, H\u00fcllermeier E (2009) Combining instance-based learning and logistic regression for multi-label classification. Mach Learn 76(2\u20133):211\u2013225","journal-title":"Mach Learn"},{"key":"1436_CR3","doi-asserted-by":"crossref","unstructured":"Clare A, King RD (2001) Knowledge discovery in multi-label phenotype data. In: European conference on data mining and knowledge discovery, pp 42\u201353","DOI":"10.1007\/3-540-44794-6_4"},{"key":"1436_CR4","first-page":"279","volume":"2010","author":"K Dembczy\u0144ski","year":"2010","unstructured":"Dembczy\u0144ski K, Cheng W, H\u00fcllermeier E (2010) Bayes optimal multilabel classification via probabilistic classifier chains. ICML 2010:279\u2013286","journal-title":"ICML"},{"issue":"1\u20132","key":"1436_CR5","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10994-012-5285-8","volume":"88","author":"K Dembczy\u0144ski","year":"2012","unstructured":"Dembczy\u0144ski K, Waegeman W, Cheng W, H\u00fcllermeier E (2012) On label dependence and loss minimization in multi-label classification. Mach Learn 88(1\u20132):5\u201345","journal-title":"Mach Learn"},{"key":"1436_CR6","first-page":"294","volume":"242","author":"K Dembczynski","year":"2012","unstructured":"Dembczynski K, Waegeman W, H\u00fcllermeier E (2012) An analysis of chaining in multi-label classification. Front Artif Intell Appl 242:294\u2013299","journal-title":"Front Artif Intell Appl"},{"key":"1436_CR7","unstructured":"Elisseeff A, Weston J (2005) A kernel method for multi-labelled classification. In: ACM Conference on Research and Develop. In: Information retrieval, pp 274\u2013281"},{"key":"1436_CR8","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10994-008-5064-8","volume":"73","author":"J F\u00fcrnkranz","year":"2008","unstructured":"F\u00fcrnkranz J, H\u00fcllermeier E, Loza Menc\u00eda E, Brinker K (2008) Multilabel classification via calibrated label ranking. Mach Learn 73:133\u2013153","journal-title":"Mach Learn"},{"issue":"12","key":"1436_CR9","first-page":"2677","volume":"9","author":"S Garc\u00eda","year":"2008","unstructured":"Garc\u00eda S, Herrera F (2008) An extension on \u201cstatistical comparisons of classifiers over multiple data sets\u201d for all pairwise comparisons. J Machine Learn Res 9(12):2677\u20132694","journal-title":"J Machine Learn Res"},{"key":"1436_CR10","doi-asserted-by":"crossref","unstructured":"Ghamrawi N, McCallum A (2005) Collective multi-label classification. In: ACM International Conference on Information and Knowledge Management. ACM, pp 195\u2013200","DOI":"10.21236\/ADA440081"},{"issue":"3","key":"1436_CR11","doi-asserted-by":"publisher","first-page":"52:1","DOI":"10.1145\/2716262","volume":"47","author":"E Gibaja","year":"2015","unstructured":"Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surv 47(3):52:1\u201352:38. https:\/\/doi.org\/10.1145\/2716262","journal-title":"ACM Comput Surv"},{"key":"1436_CR12","doi-asserted-by":"crossref","unstructured":"Godbole S, Sarawagi S (2004) Discriminative methods for multi-labeled classification. In: Pacific-Asia conference on knowledge discovery and data mining, pp 22\u201330","DOI":"10.1007\/978-3-540-24775-3_5"},{"key":"1436_CR13","doi-asserted-by":"publisher","unstructured":"Goncalves EC, Plastino A, Freitas AA (2013) A genetic algorithm for optimizing the label ordering in multi-label classifier chains. In: IEEE 25th international conference on tools with artificial intelligence, pp 469\u2013476. https:\/\/doi.org\/10.1109\/ICTAI.2013.76","DOI":"10.1109\/ICTAI.2013.76"},{"key":"1436_CR14","first-page":"665","volume":"2012","author":"A Kumar","year":"2012","unstructured":"Kumar A, Vembu S, Menon AK, Elkan C (2012) Learning and inference in probabilistic classifier chains with beam search. ECML\/PKDD 2012:665\u2013680","journal-title":"ECML\/PKDD"},{"issue":"1","key":"1436_CR15","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s10994-013-5371-6","volume":"92","author":"A Kumar","year":"2013","unstructured":"Kumar A, Vembu S, Menon AK, Elkan C (2013) Beam search algorithms for multi-label learning. Mach Learn 92(1):65\u201389","journal-title":"Mach Learn"},{"issue":"Apr","key":"1436_CR16","first-page":"627","volume":"9","author":"CJ Lin","year":"2008","unstructured":"Lin CJ, Weng RC, Keerthi SS (2008) Trust region Newton method for logistic regression. J Machine Learn Res 9(Apr):627\u2013650","journal-title":"J Machine Learn Res"},{"key":"1436_CR17","unstructured":"McCallum AK (1999) Multi-label text classification with a mixture model trained by em. In: AAAI 99 workshop on text learning"},{"key":"1436_CR18","unstructured":"Mena D, Monta\u00f1\u00e9s E, Quevedo JR, Del\u00a0Coz JJ (2015) Using A* for inference in probabilistic classifier chains. In: Proceedings of the 24th international conference on artificial intelligence, IJCAI\u201915. AAAI Press, pp 3707\u20133713"},{"issue":"6","key":"1436_CR19","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1002\/widm.1185","volume":"6","author":"D Mena","year":"2016","unstructured":"Mena D, Monta\u00f1\u00e9s E, Quevedo JR, del Coz JJ (2016) An overview of inference methods in probabilistic classifier chains for multilabel classification. Wiley Interdiscip Rev Data Min Knowl Discov 6(6):215\u2013230. https:\/\/doi.org\/10.1002\/widm.1185","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"1","key":"1436_CR20","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s10994-016-5593-5","volume":"106","author":"D Mena","year":"2017","unstructured":"Mena D, Monta\u00f1\u00e9s E, Quevedo JR, del Coz JJ (2017) A family of admissible heuristics for A* to perform inference in probabilistic classifier chains. Mach Learn 106(1):143\u2013169. https:\/\/doi.org\/10.1007\/s10994-016-5593-5","journal-title":"Mach Learn"},{"key":"1436_CR21","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.knosys.2017.03.015","volume":"126","author":"D Mena","year":"2017","unstructured":"Mena D, Quevedo JR, Monta\u00f1\u00e9s E, del Coz JJ (2017) A heuristic in A* for inference in nonlinear probabilistic classifier chains. Knowl-Based Syst 126:78\u201390. https:\/\/doi.org\/10.1016\/j.knosys.2017.03.015","journal-title":"Knowl-Based Syst"},{"key":"1436_CR22","doi-asserted-by":"crossref","unstructured":"Monta\u00f1\u00e9s E, Quevedo J, del Coz JJ (2011) Aggregating independent and dependent models to learn multi-label classifiers. In: ECML\u201911, pp 484\u2013500","DOI":"10.1007\/978-3-642-23783-6_31"},{"issue":"3","key":"1436_CR23","doi-asserted-by":"publisher","first-page":"1494","DOI":"10.1016\/j.patcog.2013.09.029","volume":"47","author":"E Monta\u00f1\u00e9s","year":"2014","unstructured":"Monta\u00f1\u00e9s E, Senge R, Barranquero J, Quevedo J, del Coz JJ, H\u00fcllermeier E (2014) Dependent binary relevance models for multi-label classification. Pattern Recogn 47(3):1494\u20131508","journal-title":"Pattern Recogn"},{"key":"1436_CR24","doi-asserted-by":"publisher","unstructured":"Prati R (2015) Fuzzy rule classifiers for multi-label classification. https:\/\/doi.org\/10.1109\/FUZZ-IEEE.2015.7337815","DOI":"10.1109\/FUZZ-IEEE.2015.7337815"},{"key":"1436_CR25","unstructured":"Qi GJ, Hua XS, Rui Y, Tang J, Mei T, Zhang HJ (2007) Correlative multi-label video annotation. In: Proceedings of the international conference on multimedia. ACM, New York, pp 17\u201326"},{"key":"1436_CR26","unstructured":"Read J, Martino L, Hollm\u00e9n J (2016) Multi-label methods for prediction with sequential data. CoRR arXiv:1609.08349"},{"issue":"3","key":"1436_CR27","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1016\/j.patcog.2013.10.006","volume":"47","author":"J Read","year":"2014","unstructured":"Read J, Martino L, Luengo D (2014) Efficient monte carlo methods for multi-dimensional learning with classifier chains. Pattern Recogn 47(3):1535\u20131546","journal-title":"Pattern Recogn"},{"issue":"6","key":"1436_CR28","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1016\/j.patcog.2015.01.004","volume":"48","author":"J Read","year":"2015","unstructured":"Read J, Martino L, Olmos PM, Luengo D (2015) Scalable multi-output label prediction: from classifier chains to classifier trellises. Pattern Recogn 48(6):2096\u20132109. https:\/\/doi.org\/10.1016\/j.patcog.2015.01.004","journal-title":"Pattern Recogn"},{"key":"1436_CR29","doi-asserted-by":"crossref","unstructured":"Read J, Pfahringer B, Holmes G (2008) Multi-label classification using ensembles of pruned sets. In: IEEE International Conference on Data Mining, pp 995\u20131000. IEEE","DOI":"10.1109\/ICDM.2008.74"},{"key":"1436_CR30","doi-asserted-by":"crossref","unstructured":"Read J, Pfahringer B, Holmes G, Frank E (2009) Classifier chains for multi-label classification. In: ECML\/PKDD\u201909, LNCS. Springer, pp 254\u2013269","DOI":"10.1007\/978-3-642-04174-7_17"},{"issue":"3","key":"1436_CR31","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333\u2013359","journal-title":"Mach Learn"},{"key":"1436_CR32","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1023\/A:1007649029923","volume":"39","author":"RE Schapire","year":"2000","unstructured":"Schapire RE, Singer Y (2000) Boostexter: a boosting-based system for text categorization. Machine Learn 39:135\u2013168","journal-title":"Machine Learn"},{"key":"1436_CR33","unstructured":"Tsoumakas G, Katakis I, Vlahavas I (2008) Effective and efficient multilabel classification in domains with large number of labels. In: Proceedings of the ECML\/PKDD 2008 Workshop on Mining Multidimensional Data (MMD\u201908), vol. 21. Antwerp, Belgium, pp 53\u201359"},{"key":"1436_CR34","unstructured":"Tsoumakas G, Vlahavas I (2007) Random k-Labelsets: An ensemble method for multi-label classification. In: ECML\/PKDD\u201907. Springer, pp 406\u2013417"},{"issue":"5","key":"1436_CR35","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s10994-016-5600-x","volume":"106","author":"YP Wu","year":"2017","unstructured":"Wu YP, Lin HT (2017) Progressive random k-labelsets for cost-sensitive multi-label classification. Mach Learn 106(5):671\u2013694. https:\/\/doi.org\/10.1007\/s10994-016-5600-x","journal-title":"Mach Learn"},{"key":"1436_CR36","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/TKDE.2006.162","volume":"18","author":"ML Zhang","year":"2006","unstructured":"Zhang ML, Zhou ZH (2006) Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans Knowl Data Eng 18:1338\u20131351","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"7","key":"1436_CR37","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang ML, Zhou ZH (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038\u20132048","journal-title":"Pattern Recogn"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-020-01436-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-020-01436-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-020-01436-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:49:57Z","timestamp":1611881397000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-020-01436-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,30]]},"references-count":37,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2020,7]]}},"alternative-id":["1436"],"URL":"https:\/\/doi.org\/10.1007\/s10115-020-01436-5","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,30]]},"assertion":[{"value":"19 January 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}