{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T05:26:46Z","timestamp":1775021206663,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076116"],"award-info":[{"award-number":["62076116"]}],"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":["2021J02049"],"award-info":[{"award-number":["2021J02049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2020J01811"],"award-info":[{"award-number":["2020J01811"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2020J01792"],"award-info":[{"award-number":["2020J01792"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s13042-022-01609-4","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T05:02:35Z","timestamp":1661230955000},"page":"3509-3522","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multi-label causal feature selection based on neighbourhood mutual information"],"prefix":"10.1007","volume":"13","author":[{"given":"Jie","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yaojin","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Longzhu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yun-an","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Meiyan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Jinkun","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"1609_CR1","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.jbi.2018.08.009","volume":"86","author":"JF Samah","year":"2018","unstructured":"Samah JF, Aditya T (2018) Exploiting MEDLINE for gene molecular function prediction via NMF based multi-label classification. J Biomed Inform 86:160\u2013166","journal-title":"J Biomed Inform"},{"key":"1609_CR2","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.patcog.2018.01.022","volume":"78","author":"Y Liu","year":"2018","unstructured":"Liu Y, Wen K, Gao Q et al (2018) SVM based multi-label learning with missing labels for image annotation. Pattern Recognit 78:307\u2013317","journal-title":"Pattern Recognit"},{"key":"1609_CR3","doi-asserted-by":"crossref","unstructured":"Liu J, Wang C, Wu Y et al (2017) Deep learning for extreme multi-label text classification. In: Proceedings of the 40th international ACM conference on research and development in information retrieval, pp 115\u2013124","DOI":"10.1145\/3077136.3080834"},{"issue":"10","key":"1609_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2016.2581161","volume":"28","author":"Q Wu","year":"2016","unstructured":"Wu Q, Tan M, Song H et al (2016) Ml-forest: a multi-label tree ensemble method for multi-label classification. IEEE Trans Knowl Data Eng 28(10):1\u20131","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"30","key":"1609_CR5","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neucom.2015.06.010","volume":"168","author":"Y Lin","year":"2015","unstructured":"Lin Y, Hu Q, Liu J et al (2015) Multi-label feature selection based on max-dependency and min-redundancy. Neurocomputing 168(30):92\u2013103","journal-title":"Neurocomputing"},{"issue":"1","key":"1609_CR6","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/s10462-016-9516-4","volume":"49","author":"RB Pereira","year":"2018","unstructured":"Pereira RB, Plastino A, Zadrozny B et al (2018) Categorizing feature selection methods for multi-label classification. Artif Intell Rev 49(1):57\u201378","journal-title":"Artif Intell Rev"},{"key":"1609_CR7","doi-asserted-by":"publisher","unstructured":"Huang R, Kang L (2021) Local positive and negative label correlation analysis with label awareness for multi-label classification. Int J Mach Learn. https:\/\/doi.org\/10.1007\/s13042-021-01352-2","DOI":"10.1007\/s13042-021-01352-2"},{"key":"1609_CR8","doi-asserted-by":"publisher","unstructured":"Karagoz G, Yazici A, Dokeroglu T et al (2021) A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data. Int J Mach Learn. https:\/\/doi.org\/10.1007\/s13042-020-01156-w","DOI":"10.1007\/s13042-020-01156-w"},{"key":"1609_CR9","doi-asserted-by":"crossref","unstructured":"Godbole S, Sarawagi S (2004) Discriminative methods for multi-labeled classification. In: Conference on advances in knowledge discovery and data. Berlin, Heidelberg, pp 22\u201330","DOI":"10.1007\/978-3-540-24775-3_5"},{"key":"1609_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1839490.1839495","volume":"4","author":"Y Zhang","year":"2010","unstructured":"Zhang Y, Zhou Z (2010) Multilabel dimensionality reduction via dependence maximization. ACM Trans Knowl Discov Data 4:1\u201321","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1609_CR11","unstructured":"Jian L, Li J, Shu, K et al (2016) Multi-label informed feature selection. In: Proceedings of 26th international joint conference on artificial intelligence. New York, New York, pp 1627\u20131633"},{"issue":"1","key":"1609_CR12","first-page":"171","volume":"11","author":"CF Aliferis","year":"2010","unstructured":"Aliferis CF, Statnikov AR, Tsamardinos I et al (2010) Local causal and Markov blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation. J Mach Learn Res 11(1):171\u2013234","journal-title":"J Mach Learn Res"},{"issue":"9","key":"1609_CR13","doi-asserted-by":"publisher","first-page":"2240","DOI":"10.1109\/TPAMI.2019.2908373","volume":"42","author":"K Yu","year":"2020","unstructured":"Yu K, Liu L, Li J et al (2020) Multi-source causal feature selection. IEEE Trans Pattern Anal Mach Intell 42(9):2240\u20132256","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"1609_CR14","first-page":"235","volume":"11","author":"CF Aliferis","year":"2010","unstructured":"Aliferis CF, Statnikov A, Tsamardinos I et al (2010) Local causal and Markov blanket induction for causal discovery and feature selection for classification part II: analysis and extensions. J Mach Learn Res 11(10):235\u2013284","journal-title":"J Mach Learn Res"},{"key":"1609_CR15","unstructured":"Tsamardinos I, Aliferis CF (2003) Towards principled feature selection: relevancy, filters and wrappers. In: Proceedings of the 9th international workshop on artificial intelligence and statistics"},{"key":"1609_CR16","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.knosys.2012.04.028","volume":"35","author":"RM Andrs","year":"2012","unstructured":"Andrs RM, Serafn M (2012) A Bayesian stochastic search method for discovering Markov boundaries. Knowl Based Syst 35:211\u2013223","journal-title":"Knowl Based Syst"},{"key":"1609_CR17","first-page":"499","volume":"14","author":"A Statnikov","year":"2013","unstructured":"Statnikov A, Lytkin NI, Lemeire J et al (2013) Algorithms for discovery of multiple Markov boundaries. J Mach Learn Res 14:499\u2013566","journal-title":"J Mach Learn Res"},{"issue":"9","key":"1609_CR18","first-page":"1295","volume":"9","author":"JP Pellet","year":"2008","unstructured":"Pellet JP, Elisseeff A (2008) Using Markov blankets for causal structure learning. J Mach Learn Res 9(9):1295\u20131342","journal-title":"J Mach Learn Res"},{"key":"1609_CR19","doi-asserted-by":"crossref","unstructured":"Wu X, Jiang B, Yu K et al (2020) Multi-label causal feature selection. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, no 4, pp 6430\u20136437","DOI":"10.1609\/aaai.v34i04.6114"},{"issue":"1","key":"1609_CR20","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1109\/TPAMI.2014.2339815","volume":"37","author":"M Zhang","year":"2015","unstructured":"Zhang M, Wu L (2015) Lift: multi-label learning with label-specific features. IEEE Trans Pattern Anal Mach Intell 37(1):107\u2013120","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1609_CR21","unstructured":"Wu X, Jiang B, Zhong Y et al (2020) Multi-label causal variable discovery: learning common causal variables and label-specific causal variables. https:\/\/arxiv.org\/abs\/2011.04176. Accessed 9 Nov 2020"},{"key":"1609_CR22","first-page":"41","volume":"107","author":"YS Wang","year":"2020","unstructured":"Wang YS, Drton M (2020) High-dimensional causal discovery under non-Gaussianity. Biometrika 107:41\u201359","journal-title":"Biometrika"},{"key":"1609_CR23","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.neunet.2013.01.025","volume":"43","author":"R Cai","year":"2013","unstructured":"Cai R, Zhang Z, Hao Z (2013) Causal gene identification using combinatorial V-structure search. Neural Netw 43:63\u201371","journal-title":"Neural Netw"},{"issue":"12","key":"1609_CR24","doi-asserted-by":"publisher","first-page":"4983","DOI":"10.1109\/TCYB.2019.2940509","volume":"50","author":"X Wu","year":"2020","unstructured":"Wu X, Jiang B, Yu K (2020) Accurate Markov boundary discovery for causal feature selection. IEEE Trans Cybern 50(12):4983\u20134996","journal-title":"IEEE Trans Cybern"},{"key":"1609_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-2748-9","volume-title":"Causation, prediction, and search","author":"P Spirtes","year":"1993","unstructured":"Spirtes P, Glymour C, Scheines R (1993) Causation, prediction, and search. Springer, New York"},{"key":"1609_CR26","volume-title":"Probabilistic reasoning in intelligent systems: networks of plausible inference","author":"J Pearl","year":"1988","unstructured":"Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann Publishers Inc, San Francisco"},{"issue":"3","key":"1609_CR27","first-page":"111","volume":"31","author":"RE Neapolitan","year":"2007","unstructured":"Neapolitan RE, Xia J (2007) Learning Bayesian networks\u2014ScienceDirect. Probab Methods Financ Mark Inform 31(3):111\u2013175","journal-title":"Probab Methods Financ Mark Inform"},{"key":"1609_CR28","volume-title":"Probabilistic reasoning in intelligent systems: networks of plausible inference","author":"J Pearl","year":"2014","unstructured":"Pearl J (2014) Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier, Amsterdam"},{"issue":"5","key":"1609_CR29","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1109\/TCYB.2016.2539338","volume":"47","author":"T Gao","year":"2017","unstructured":"Gao T, Ji Q (2017) Efficient Markov blanket discovery and its application. IEEE Trans Cybern 47(5):1169\u20131179","journal-title":"IEEE Trans Cybern"},{"issue":"9","key":"1609_CR30","doi-asserted-by":"publisher","first-page":"10737","DOI":"10.1016\/j.eswa.2011.01.023","volume":"38","author":"Q Hu","year":"2011","unstructured":"Hu Q, Zhang L, Zhang D et al (2011) Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Syst Appl Int J 38(9):10737\u201310750","journal-title":"Expert Syst Appl Int J"},{"issue":"8","key":"1609_CR31","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"M Zhang","year":"2014","unstructured":"Zhang M, Zhou Z (2014) A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 26(8):1819\u20131837","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1609_CR32","unstructured":"Aliferis CF, Tsamardinos I, Statnikov A (2003) HITON: a novel Markov blanket algorithm for optimal variable selection. In: AMIA annual symposium proceedings 2003"},{"issue":"1","key":"1609_CR33","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/TNNLS.2020.3027745","volume":"33","author":"J Ma","year":"2020","unstructured":"Ma J, Chow T (2020) Topic-based instance and feature selection in multilabel classification. IEEE Trans Neural Netw Learn Syst 33(1):315\u2013329","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1609_CR34","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1016\/j.patrec.2018.08.021","volume":"112","author":"R Huang","year":"2018","unstructured":"Huang R, Jiang W, Sun G (2018) Manifold-based constraint Laplacian score for multi-label feature selection. Pattern Recognit Lett 112:346\u2013352","journal-title":"Pattern Recognit Lett"},{"key":"1609_CR35","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.patrec.2012.10.005","volume":"34","author":"J Lee","year":"2013","unstructured":"Lee J, Kim D (2013) Feature selection for multi-label classification using multivariate mutual information. Pattern Recognit Lett 34:349\u2013357","journal-title":"Pattern Recognit Lett"},{"key":"1609_CR36","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"M Zhang","year":"2007","unstructured":"Zhang M, Zhou Z (2007) Ml-knn: a lazy learning approach to multi-label learning. Pattern Recognit 40:2038\u20132048","journal-title":"Pattern Recognit"},{"key":"1609_CR37","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1023\/A:1007649029923","volume":"39","author":"R Schapire","year":"2000","unstructured":"Schapire R, Singer Y (2000) BoosTexter: a boosting-based system for text categorization. Mach Learn 39:125\u2013168","journal-title":"Mach Learn"},{"issue":"15","key":"1609_CR38","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.knosys.2018.04.004","volume":"152","author":"Y Lin","year":"2018","unstructured":"Lin Y, Li Y, Wang C et al (2018) Attribute reduction for multi-label learning with fuzzy rough set. Knowl Based Syst 152(15):51\u201361","journal-title":"Knowl Based Syst"},{"issue":"4","key":"1609_CR39","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1111\/1467-9280.00157","volume":"10","author":"A Tellegen","year":"1999","unstructured":"Tellegen A, Watson D, Clark LA (1999) On the dimensional and hierarchical structure of affect. Psychol Sci 10(4):297\u2013303","journal-title":"Psychol Sci"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01609-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01609-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01609-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T23:05:39Z","timestamp":1664060739000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01609-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,23]]},"references-count":39,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["1609"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01609-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,23]]},"assertion":[{"value":"20 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}