{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:22:41Z","timestamp":1771698161529,"version":"3.50.1"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T00:00:00Z","timestamp":1739232000000},"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":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1007\/s11432-022-3728-2","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T06:53:48Z","timestamp":1739516028000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Attribute grouping-based naive Bayesian classifier"],"prefix":"10.1007","volume":"68","author":[{"given":"Yulin","family":"He","sequence":"first","affiliation":[]},{"given":"Guiliang","family":"Ou","sequence":"additional","affiliation":[]},{"given":"Philippe","family":"Fournier-Viger","sequence":"additional","affiliation":[]},{"given":"Joshua Zhexue","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"3728_CR1","volume-title":"Pattern Recognition and Machine Learning","author":"C Bishop","year":"2007","unstructured":"Bishop C. Pattern Recognition and Machine Learning. Berlin: Springer, 2007"},{"key":"3728_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","volume":"14","author":"X Wu","year":"2008","unstructured":"Wu X, Kumar V, Quinlan J R, et al. Top 10 algorithms in data mining. Knowl Inf Syst, 2008, 14: 1\u201337","journal-title":"Knowl Inf Syst"},{"key":"3728_CR3","first-page":"41","volume-title":"Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence","author":"I Rish","year":"2001","unstructured":"Rish I. An empirical study of the naive Bayes classifier. In: Proceedings of the IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 2001. 41\u201346"},{"key":"3728_CR4","first-page":"37","volume":"5","author":"S L Ting","year":"2011","unstructured":"Ting S L, Ip W H, Tsang A H. Is naive Bayes a good classifier for document classification. Int J Softw Eng Appl, 2011, 5: 37\u201346","journal-title":"Int J Softw Eng Appl"},{"key":"3728_CR5","first-page":"3650","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"S McCann","year":"2012","unstructured":"McCann S, Lowe D G. Local naive Bayes nearest neighbor for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012. 3650\u20133656"},{"key":"3728_CR6","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.protcy.2012.05.017","volume":"4","author":"S Mukherjee","year":"2012","unstructured":"Mukherjee S, Sharma N. Intrusion detection using naive Bayes classifier with feature reduction. Procedia Tech, 2012, 4: 119\u2013128","journal-title":"Procedia Tech"},{"key":"3728_CR7","first-page":"99","volume-title":"Proceedings of the IEEE International Conference on Big Data","author":"B Liu","year":"2013","unstructured":"Liu B, Blasch E, Chen Y, et al. Scalable sentiment classification for big data analysis using naive Bayes classifier. In: Proceedings of the IEEE International Conference on Big Data, 2013. 99\u2013104"},{"key":"3728_CR8","first-page":"1","volume-title":"Proceedings of the International Conference on Computer Communication and Informatics","author":"A Prabhat","year":"2017","unstructured":"Prabhat A, Khullar V. Sentiment classification on big data using naive Bayes and logistic regression. In: Proceedings of the International Conference on Computer Communication and Informatics, 2017. 1\u20135"},{"key":"3728_CR9","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.bdr.2018.05.007","volume":"14","author":"N Sun","year":"2018","unstructured":"Sun N, Sun B, Lin J D, et al. Lossless pruned naive Bayes for big data classifications. Big Data Res, 2018, 14: 27\u201336","journal-title":"Big Data Res"},{"key":"3728_CR10","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/BF00994110","volume":"9","author":"G F Cooper","year":"1992","unstructured":"Cooper G F, Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach Learn, 1992, 9: 309\u2013347","journal-title":"Mach Learn"},{"key":"3728_CR11","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1023\/A:1007465528199","volume":"29","author":"N Friedman","year":"1997","unstructured":"Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn, 1997, 29: 131\u2013163","journal-title":"Mach Learn"},{"key":"3728_CR12","first-page":"1","volume-title":"Proceedings of the International Workshop on Artificial Intelligence and Statistics, Florida","author":"E J Keogh","year":"1999","unstructured":"Keogh E J, Pazzani M J. Learning augmented Bayesian classifiers: a comparison of distribution-based and classification-based approaches. In: Proceedings of the International Workshop on Artificial Intelligence and Statistics, Florida, 1999. 1\u20136"},{"key":"3728_CR13","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10994-005-4258-6","volume":"58","author":"G I Webb","year":"2005","unstructured":"Webb G I, Boughton J R, Wang Z. Not so naive Bayes: aggregating one-dependence estimators. Mach Learn, 2005, 58: 5\u201324","journal-title":"Mach Learn"},{"key":"3728_CR14","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1080\/0952813X.2011.639092","volume":"24","author":"L X Jiang","year":"2012","unstructured":"Jiang L X, Zhang H, Cai Z H, et al. Weighted average of one-dependence estimators. J Exp Theor Artif Intell, 2012, 24: 219\u2013230","journal-title":"J Exp Theor Artif Intell"},{"key":"3728_CR15","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1109\/TPAMI.2011.149","volume":"34","author":"F Pernkopf","year":"2012","unstructured":"Pernkopf F, Wohlmayr M, Tschiatschek S. Maximum margin Bayesian network classifiers. IEEE Trans Pattern Anal Mach Intell, 2012, 34: 521\u2013532","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3728_CR16","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"X Fan","year":"2015","unstructured":"Fan X, Yuan C. An improved lower bound for Bayesian network structure learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2015"},{"key":"3728_CR17","doi-asserted-by":"publisher","first-page":"1361","DOI":"10.1109\/TKDE.2008.234","volume":"21","author":"L X Jiang","year":"2009","unstructured":"Jiang L X, Zhang H, Cai H Z H. A novel Bayes model: hidden naive Bayes. IEEE Trans Knowl Data Eng, 2009, 21: 1361\u20131371","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3728_CR18","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/TKDE.2018.2836440","volume":"31","author":"L X Jiang","year":"2019","unstructured":"Jiang L X, Zhang L G, Li C Q, et al. A correlation-based feature weighting filter for naive Bayes. IEEE Trans Knowl Data Eng, 2019, 31: 201\u2013213","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3728_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.patcog.2018.11.032","volume":"88","author":"L X Jiang","year":"2019","unstructured":"Jiang L X, Zhang L G, Yu L J, et al. Class-specific attribute weighted naive Bayes. Pattern Recogn, 2019, 88: 321\u2013330","journal-title":"Pattern Recogn"},{"key":"3728_CR20","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ijar.2008.08.008","volume":"50","author":"A P\u00e9rez","year":"2009","unstructured":"P\u00e9rez A, Larra\u00f1aga P, Inza I. Bayesian classifiers based on kernel density estimation: flexible classifiers. Int J Approx Reason, 2009, 50: 341\u2013362","journal-title":"Int J Approx Reason"},{"key":"3728_CR21","first-page":"1","volume-title":"Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence","author":"G John","year":"1995","unstructured":"John G, Langley P. Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, Quebec, 1995. 1\u20138"},{"key":"3728_CR22","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TCYB.2013.2245891","volume":"44","author":"X Z Wang","year":"2014","unstructured":"Wang X Z, He Y L, Wang D D. Non-naive Bayesian classifiers for classification problems with continuous attributes. IEEE Trans Cybern, 2014, 44: 21\u201339","journal-title":"IEEE Trans Cybern"},{"key":"3728_CR23","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.ins.2013.09.003","volume":"259","author":"Y L He","year":"2014","unstructured":"He Y L, Wang R, Kwong S, et al. Bayesian classifiers based on probability density estimation and their applications to simultaneous fault diagnosis. Inform Sci, 2014, 259: 252\u2013268","journal-title":"Inform Sci"},{"key":"3728_CR24","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.ins.2019.01.026","volume":"482","author":"Z Q Geng","year":"2019","unstructured":"Geng Z Q, Meng Q C, Bai J, et al. A model-free Bayesian classifier. Inform Sci, 2019, 482: 171\u2013188","journal-title":"Inform Sci"},{"key":"3728_CR25","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.artint.2015.03.003","volume":"244","author":"M Bartlett","year":"2017","unstructured":"Bartlett M, Cussens J. Integer linear programming for the Bayesian network structure learning problem. Artif Intell, 2017, 244: 258\u2013271","journal-title":"Artif Intell"},{"key":"3728_CR26","first-page":"1193","volume-title":"Proceedings of the International Conference on Machine Learning","author":"T Gao","year":"2017","unstructured":"Gao T, Fadnis K, Campbell M. Local-to-global Bayesian network structure learning. In: Proceedings of the International Conference on Machine Learning, 2017. 1193\u20131202"},{"key":"3728_CR27","doi-asserted-by":"publisher","first-page":"106422","DOI":"10.1016\/j.knosys.2020.106422","volume":"208","author":"Y Liu","year":"2020","unstructured":"Liu Y, Wang L M, Mammadov M. Learning semi-lazy Bayesian network classifier under the c.i.i.d assumption. Knowl-Based Syst, 2020, 208: 106422","journal-title":"Knowl-Based Syst"},{"key":"3728_CR28","doi-asserted-by":"publisher","first-page":"3766","DOI":"10.3390\/e17063766","volume":"17","author":"L M Wang","year":"2015","unstructured":"Wang L M, Zhao H Y. Learning a flexible K-dependence Bayesian classifier from the chain rule of joint probability distribution. Entropy, 2015, 17: 3766\u20133786","journal-title":"Entropy"},{"key":"3728_CR29","doi-asserted-by":"publisher","first-page":"3604","DOI":"10.1007\/s10489-021-02531-y","volume":"52","author":"L M Wang","year":"2022","unstructured":"Wang L M, Zhang X H, Li K, et al. Semi-supervised learning for K-dependence Bayesian classifiers. Appl Intell, 2022, 52: 3604\u20133622","journal-title":"Appl Intell"},{"key":"3728_CR30","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10994-011-5263-6","volume":"86","author":"G I Webb","year":"2012","unstructured":"Webb G I, Boughton J R, Zheng F, et al. Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. Mach Learn, 2012, 86: 233\u2013272","journal-title":"Mach Learn"},{"key":"3728_CR31","doi-asserted-by":"publisher","first-page":"107288","DOI":"10.1016\/j.knosys.2021.107288","volume":"228","author":"T T Wong","year":"2021","unstructured":"Wong T T, Tsai H C. Multinomial naive Bayesian classifier with generalized Dirichlet priors for high-dimensional imbalanced data. Knowl-Based Syst, 2021, 228: 107288","journal-title":"Knowl-Based Syst"},{"key":"3728_CR32","first-page":"567","volume-title":"Proceedings of the 4th IEEE International Conference on Data Mining","author":"H Zhang","year":"2004","unstructured":"Zhang H, Sheng S L. Learning weighted naive Bayes with accurate ranking. In: Proceedings of the 4th IEEE International Conference on Data Mining, 2004. 567\u2013570"},{"key":"3728_CR33","first-page":"1","volume-title":"Proceedings of the Ibero-American Conference on Artificial Intelligence","author":"M Bressan","year":"2002","unstructured":"Bressan M, Vitria J. Improving naive Bayes using class-conditional ICA. In: Proceedings of the Ibero-American Conference on Artificial Intelligence, 2002. 1\u201310"},{"key":"3728_CR34","first-page":"1004","volume-title":"Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition","author":"M Bressan","year":"2001","unstructured":"Bressan M, Guillamet D, Vitria J. Using an ICA representation of high dimensional data for object recognition and classification. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. 1004\u20131009"},{"key":"3728_CR35","first-page":"4873","volume":"28","author":"F Qin","year":"2007","unstructured":"Qin F, Ren S L, Cheng Z K, et al. Bayes classification model based on ICA. Comput Eng Des, 2007, 28: 4873\u20134877","journal-title":"Comput Eng Des"},{"key":"3728_CR36","first-page":"16","volume-title":"Proceedings of the International Work-Conference on Artificial Neural Networks","author":"L Fan","year":"2007","unstructured":"Fan L, Poh K L. A comparative study of PCA, ICA and class-conditional ICA for naive Bayes classifier. In: Proceedings of the International Work-Conference on Artificial Neural Networks, 2007. 16\u201322"},{"key":"3728_CR37","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold S, Esbensen K, Geladi P. Principal component analysis. Chemometr Intell Lab Syst, 1987, 2: 37\u201352","journal-title":"Chemometr Intell Lab Syst"},{"key":"3728_CR38","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","volume":"36","author":"P Comon","year":"1994","unstructured":"Comon P. Independent component analysis, a new concept? Signal Process, 1994, 36: 287\u2013314","journal-title":"Signal Process"},{"key":"3728_CR39","first-page":"3","volume":"41","author":"S K Jayanthi","year":"2014","unstructured":"Jayanthi S K, Sasikala S. Naive Bayesian classifier and PCA for web link spam detection. Comput Sci Telecommun, 2014, 41: 3\u201315","journal-title":"Comput Sci Telecommun"},{"key":"3728_CR40","first-page":"1","volume":"2018","author":"B Zhang","year":"2018","unstructured":"Zhang B, Liu Z Y, Jia Y G, et al. Network intrusion detection method based on PCA and Bayes algorithm. Secur Commun Netw, 2018, 2018: 1\u201311","journal-title":"Secur Commun Netw"},{"key":"3728_CR41","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","volume":"33","author":"E Parzen","year":"1962","unstructured":"Parzen E. On estimation of a probability density function and mode. Ann Math Statist, 1962, 33: 1065\u20131076","journal-title":"Ann Math Statist"},{"key":"3728_CR42","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.patcog.2011.06.004","volume":"45","author":"X J Chen","year":"2012","unstructured":"Chen X J, Ye Y M, Xu X F, et al. A feature group weighting method for subspace clustering of high-dimensional data. Pattern Recogn, 2012, 45: 434\u2013446","journal-title":"Pattern Recogn"},{"key":"3728_CR43","doi-asserted-by":"publisher","first-page":"3703","DOI":"10.1016\/j.patcog.2015.05.016","volume":"48","author":"G Gan","year":"2015","unstructured":"Gan G, Ng M K P. Subspace clustering with automatic feature grouping. Pattern Recogn, 2015, 48: 3703\u20133713","journal-title":"Pattern Recogn"},{"key":"3728_CR44","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1109\/72.471375","volume":"6","author":"B Igelnik","year":"1995","unstructured":"Igelnik B, Pao Y H. Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans Neural Netw, 1995, 6: 1320\u20131329","journal-title":"IEEE Trans Neural Netw"},{"key":"3728_CR45","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ins.2016.01.039","volume":"364","author":"L Zhang","year":"2016","unstructured":"Zhang L, Suganthan P N. A survey of randomized algorithms for training neural networks. Inform Sci, 2016, 364: 146\u2013155","journal-title":"Inform Sci"},{"key":"3728_CR46","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.ins.2015.09.025","volume":"367","author":"L Zhang","year":"2016","unstructured":"Zhang L, Suganthan P N. A comprehensive evaluation of random vector functional link networks. Inform Sci, 2016, 367: 1094\u20131105","journal-title":"Inform Sci"},{"key":"3728_CR47","first-page":"255","volume":"17","author":"J Alcala-Fdez","year":"2011","unstructured":"Alcala-Fdez J, Fernandez A, Luengo J, et al. KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multi-Valued Logic Soft Comput, 2011, 17: 255\u2013287","journal-title":"J Multi-Valued Logic Soft Comput"},{"key":"3728_CR48","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1023\/A:1010920819831","volume":"45","author":"D J Hand","year":"2001","unstructured":"Hand D J, Till R J. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach Learn, 2001, 45: 171\u2013186","journal-title":"Mach Learn"},{"key":"3728_CR49","first-page":"1","volume":"7","author":"J Demar","year":"2006","unstructured":"Demar J. Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res, 2006, 7: 1\u201330","journal-title":"J Mach Learn Res"},{"key":"3728_CR50","doi-asserted-by":"publisher","first-page":"5846","DOI":"10.1109\/TII.2019.2912723","volume":"15","author":"S Salloum","year":"2019","unstructured":"Salloum S, Huang J Z, He Y. Random sample partition: a distributed data model for big data analysis. IEEE Trans Ind Inf, 2019, 15: 5846\u20135854","journal-title":"IEEE Trans Ind Inf"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-022-3728-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-022-3728-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-022-3728-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T06:53:56Z","timestamp":1739516036000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-022-3728-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,11]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["3728"],"URL":"https:\/\/doi.org\/10.1007\/s11432-022-3728-2","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,11]]},"assertion":[{"value":"20 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 October 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"132106"}}