{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:23:32Z","timestamp":1785511412532,"version":"3.56.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2012,4,17]],"date-time":"2012-04-17T00:00:00Z","timestamp":1334620800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/2.0"},{"start":{"date-parts":[[2012,4,17]],"date-time":"2012-04-17T00:00:00Z","timestamp":1334620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/2.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Braz Comput Soc"],"published-print":{"date-parts":[[2012,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Regular data classification techniques are based mainly on two strong assumptions: (1)\u00a0the existence of a reasonably large labeled set of data to be used in training; and (2)\u00a0future input data instances conform to the distribution of the training set, i.e. data distribution is stationary along time. However, in the case of data stream classification, both of the aforementioned assumptions are difficult to satisfy. In this paper, we present a graph-based semi-supervised approach that extends the static classifier based on the <jats:italic>K-associated Optimal Graph<\/jats:italic> to perform online semi-supervised classification tasks. In order to learn from labeled and unlabeled patterns, here we adapt the optimal graph construction to simultaneously spread the labels in the training set. The sparse, disconnected nature of the proposed graph structure gives flexibility to cope with non-stationary classification. Experimental comparison between the proposed method and three state-of-the-art ensemble classification methods is provided and promising results have been obtained.<\/jats:p>","DOI":"10.1007\/s13173-012-0072-8","type":"journal-article","created":{"date-parts":[[2012,4,16]],"date-time":"2012-04-16T14:27:45Z","timestamp":1334586465000},"page":"299-310","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Partially labeled data stream classification with the semi-supervised K-associated graph"],"prefix":"10.1007","volume":"18","author":[{"suffix":"Jr.","given":"Jo\u00e3o Roberto","family":"Bertini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alneu de Andrade","family":"Lopes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2012,4,17]]},"reference":[{"key":"72_CR1","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1162\/089976603321780317","volume":"15","author":"M Belkin","year":"2003","unstructured":"Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15:1373\u20131396","journal-title":"Neural Comput"},{"key":"72_CR2","first-page":"1","volume":"1","author":"M Belkin","year":"2006","unstructured":"Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 1:1\u201348","journal-title":"J Mach Learn Res"},{"key":"72_CR3","first-page":"826","volume-title":"Proceedings of the joint conference, III international workshop on web and text intelligence (WTI\u201910)","author":"JR Bertini Jr","year":"2010","unstructured":"Bertini JR Jr, Lopes A, Motta R, Zhao L (2010) Online classifier based on the optimal K-associated network. In: Proceedings of the joint conference, III international workshop on web and text intelligence (WTI\u201910), pp 826\u2013835"},{"key":"72_CR4","doi-asserted-by":"publisher","first-page":"5435","DOI":"10.1016\/j.ins.2011.07.043","volume":"181","author":"JR Bertini Jr","year":"2011","unstructured":"Bertini JR Jr, Zhao L, Motta R, Lopes A (2011) A nonparametric classification method based on K-associated graphs. Inf Sci 181:5435\u20135456","journal-title":"Inf Sci"},{"key":"72_CR5","volume-title":"Handbook of graphs and networks: from the genome to the Internet","year":"2003","unstructured":"Bornholdt S, Schuster H (eds) (2003) Handbook of graphs and networks: from the genome to the Internet, 1st edn. Wiley-VCH, Weinheim","edition":"1"},{"key":"72_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2011.119","author":"FA Breve","year":"2011","unstructured":"Breve FA, Zhao L, Quiles M, Pedrycz W, Liu J (2011) Particle competition and cooperation in networks for semi-supervised learning. IEEE Trans Knowl Data Eng. doi:10.1109\/TKDE.2011.119","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"72_CR7","volume-title":"Semi-supervised learning","year":"2006","unstructured":"Chapelle O, Zien A, Sch\u00f6lkopf B (eds) (2006) Semi-supervised learning, 1st edn. MIT Press, Cambridge","edition":"1"},{"key":"72_CR8","first-page":"203","volume":"9","author":"O Chapelle","year":"2008","unstructured":"Chapelle O, Sindhwani V, Keerthi S (2008) Optimization techniques for semi-supervised support vector machines. J Mach Learn Res 9:203\u2013233","journal-title":"J Mach Learn Res"},{"key":"72_CR9","volume-title":"Introduction to algorithms","author":"T Cormen","year":"2009","unstructured":"Cormen T, Leiserson C, Rivest R, Stein C (2009) Introduction to algorithms, 3rd edn. MIT Press, Cambridge","edition":"3"},{"issue":"1","key":"72_CR10","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1109\/TPAMI.2007.70765","volume":"30","author":"M Culp","year":"2008","unstructured":"Culp M, Michailidis G (2008) Graph-based semisupervised learning. IEEE Trans Pattern Anal Mach Intell 30(1):174\u2013179","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"72_CR11","doi-asserted-by":"publisher","first-page":"2741","DOI":"10.1109\/IJCNN.2011.6033578","volume-title":"Proceedings of international joint conference on neural networks (IJCNN\u201911)","author":"G Ditzler","year":"2011","unstructured":"Ditzler G, Polikar R (2011) Semi-supervised learning in nonstationary environments. In: Proceedings of international joint conference on neural networks (IJCNN\u201911), San Jose, CA, USA. IEEE Press, New York, pp 2741\u20132748"},{"key":"72_CR12","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1016\/j.peva.2007.06.014","volume":"64","author":"J Erman","year":"2007","unstructured":"Erman J, Mahanti A, Arlitt M, Cohen I, Williamson C (2007) Offline\/realtime traffic classification using semi-supervised learning. Perform Eval 64:1194\u20131213","journal-title":"Perform Eval"},{"key":"72_CR13","first-page":"286","volume-title":"Proceedings of the Brazilian symposium on artificial intelligence (SBIA\u201904)","author":"J Gama","year":"2004","unstructured":"Gama J, Medas P, Castillo G, Rodrigues P (2004) Learning with drift detection. In: Proceedings of the Brazilian symposium on artificial intelligence (SBIA\u201904), vol 3171. Springer, Berlin, pp 286\u2013295"},{"issue":"4","key":"72_CR14","first-page":"215","volume":"13","author":"C Giraud-Carrier","year":"2000","unstructured":"Giraud-Carrier C (2000) A note on the utility of incremental learning. AI Commun 13(4):215\u2013223","journal-title":"AI Commun"},{"key":"72_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference and prediction","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer, Berlin","edition":"2"},{"key":"72_CR16","unstructured":"Hettich S, Bay S (1999) The UCI KDD archive. University of California, Irvine, School of Information and Computer Sciences. http:\/\/kdd.ics.uci.edu\/"},{"key":"72_CR17","first-page":"367","volume-title":"Proceedings of the international conference on knowledge discovery and data mining (KDD\u201999)","author":"M Kelly","year":"1999","unstructured":"Kelly M, Hand D, Adams N (1999) The impact of changing populations on classifier performance. In: Proceedings of the international conference on knowledge discovery and data mining (KDD\u201999). ACM, New York, pp 367\u2013371"},{"key":"72_CR18","first-page":"487","volume-title":"Proceedings of the international conference on machine learning (ICML\u201900)","author":"R Klinkenberg","year":"2000","unstructured":"Klinkenberg R, Joachims T (2000) Detecting concept drift with support vector machines. In: Proceedings of the international conference on machine learning (ICML\u201900). Morgan Kaufmann, San Mateo, pp 487\u2013494"},{"key":"72_CR19","first-page":"2755","volume":"8","author":"JZ Kolter","year":"2007","unstructured":"Kolter JZ, Maloof MA (2007) Dynamic weighted majority: an ensemble method for drifting concepts. J Mach Learn Res 8:2755\u20132790","journal-title":"J Mach Learn Res"},{"key":"72_CR20","first-page":"241","volume-title":"JLMR: workshop and conference proceedings","author":"P Li","year":"2010","unstructured":"Li P, Wu X, Hu X (2010) Mining recurring concept drift with limited labeled streaming data. In: JLMR: workshop and conference proceedings, vol 13, pp 241\u2013252"},{"key":"72_CR21","series-title":"Lecture notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST)","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1007\/978-3-642-02466-5_117","volume-title":"Proceedings of the international conference on complex sciences: theory and applications (COMPLEX\u201909)","author":"AA Lopes","year":"2009","unstructured":"Lopes AA, Bertini JR Jr, Motta R, Zhao L (2009) Classification based on the optimal k-associated network. In: Proceedings of the international conference on complex sciences: theory and applications (COMPLEX\u201909). Lecture notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST), vol 4. Springer, Berlin, pp 1167\u20131177"},{"key":"72_CR22","volume-title":"Proceeding of the international conference on data mining (ICDM\u201908)","author":"M Masud","year":"2008","unstructured":"Masud M, Gao J, Khan L, Han J (2008) A practical approach to classify evolving data streams: training with limited amount of labeled data. In: Proceeding of the international conference on data mining (ICDM\u201908)"},{"key":"72_CR23","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1109\/TKDE.2009.156","volume":"22","author":"L Minku","year":"2010","unstructured":"Minku L, White A, Yao X (2010) The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans Knowl Data Eng 22:730\u2013742","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"72_CR24","first-page":"384","volume-title":"Proceedings of the international artificial intelligence and applications (ICAIA\u201907)","author":"A Narasimhamurthy","year":"2007","unstructured":"Narasimhamurthy A, Kuncheva L (2007) A framework for generating data to simulate changing environments. In: Proceedings of the international artificial intelligence and applications (ICAIA\u201907), pp 384\u2013389"},{"key":"72_CR25","doi-asserted-by":"publisher","DOI":"10.1063\/1.2956982","volume":"18","author":"M Quiles","year":"2008","unstructured":"Quiles M, Zhao L, Alonso RL, Romero RAF (2008) Particle competition for complex network community detection. Chaos 18:033107","journal-title":"Chaos"},{"key":"72_CR26","volume-title":"C4.5 programs for machine learning","author":"JR Quinlan","year":"1993","unstructured":"Quinlan JR (1993) C4.5 programs for machine learning, 1st edn. Morgan Kaufmann, San Mateo","edition":"1"},{"key":"72_CR27","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.cosrev.2007.05.001","volume":"1","author":"S Schaeffer","year":"2007","unstructured":"Schaeffer S (2007) Graph clustering. Comput Sci Rev 1:27\u201334","journal-title":"Comput Sci Rev"},{"key":"72_CR28","first-page":"502","volume-title":"Proceedings of the association for the advancement of artificial intelligence (AAAI\u201986)","author":"J Schlimmer","year":"1986","unstructured":"Schlimmer J, Granger R (1986) Beyond incremental processing: tracking concept drift. In: Proceedings of the association for the advancement of artificial intelligence (AAAI\u201986). AAAI Press, Menlo Park, pp 502\u2013507"},{"key":"72_CR29","first-page":"377","volume-title":"Proc int\u2019l conf knowledge discovery and data mining (KDD\u201901)","author":"N Street","year":"2001","unstructured":"Street N, Kim Y (2001) A streaming ensemble algorithm (SEA) for large-scale classification. In: Proc int\u2019l conf knowledge discovery and data mining (KDD\u201901). ACM, New York, pp 377\u2013382"},{"key":"72_CR30","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.patrec.2008.11.006","volume":"30","author":"J Sung","year":"2009","unstructured":"Sung J, Kim D (2009) Adaptive active appearance model with incremental learning. Pattern Recognit Lett 30:359\u2013367","journal-title":"Pattern Recognit Lett"},{"key":"72_CR31","first-page":"272","volume-title":"Proceedings of the international conference on knowledge discovery and data mining (KDD\u201999)","author":"N Syed","year":"1999","unstructured":"Syed N, Liu H, Sung K (1999) Handling concept drift in incremental learning with support vector machines. In: Proceedings of the international conference on knowledge discovery and data mining (KDD\u201999), pp 272\u2013276"},{"key":"72_CR32","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U von Luxburg","year":"2007","unstructured":"von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17:395\u2013416","journal-title":"Stat Comput"},{"key":"72_CR33","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1145\/956750.956778","volume-title":"Proc international conference on knowledge discovery and data mining (KDD\u201903)","author":"H Wang","year":"2003","unstructured":"Wang H, Fan W, Yu P, Han J (2003) Mining concept-drifting data streams using ensemble classifiers. In: Proc international conference on knowledge discovery and data mining (KDD\u201903), pp 226\u2013235"},{"issue":"1","key":"72_CR34","first-page":"69","volume":"23","author":"G Widmer","year":"1996","unstructured":"Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1):69\u2013101","journal-title":"Mach Learn"},{"key":"72_CR35","doi-asserted-by":"publisher","first-page":"2656","DOI":"10.1016\/j.patcog.2008.01.025","volume":"41","author":"C Yang","year":"2008","unstructured":"Yang C, Zhou J (2008) Non-stationary data sequence classification using online class priors estimation. Pattern Recognit 41:2656\u20132664","journal-title":"Pattern Recognit"},{"key":"72_CR36","first-page":"571","volume-title":"Proceedings of the international conference on advances in neuro-information processing (NIPS\u201908)","author":"Y Yu","year":"2008","unstructured":"Yu Y, Guo S, Lan S, Ban T (2008) Anomaly intrusion detection for evolving data stream based on semi-supervised learning. In: Proceedings of the international conference on advances in neuro-information processing (NIPS\u201908), pp 571\u2013578"},{"key":"72_CR37","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1109\/ICDM.2009.76","volume-title":"Proceedings of the ninth IEEE international conference on data mining (ICDM\u201909)","author":"P Zhang","year":"2009","unstructured":"Zhang P, Zhu X, Guo L (2009) Mining data streams with labeled and unlabeled training examples. In: Proceedings of the ninth IEEE international conference on data mining (ICDM\u201909). IEEE Press, New York, pp 627\u2013636"},{"key":"72_CR38","unstructured":"Zhu X (2008) Semi-supervised learning literature survey. Tech Rep 1530, Computer-Science, University of Wisconsin-Madison"},{"key":"72_CR39","unstructured":"Zhu X (2005) Semi-supervised learning with graphs. Tech Rep Doctoral Thesis, School of Computer Science, Carnegie Mellon University"}],"container-title":["Journal of the Brazilian Computer Society"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13173-012-0072-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13173-012-0072-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13173-012-0072-8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13173-012-0072-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T18:36:19Z","timestamp":1630521379000},"score":1,"resource":{"primary":{"URL":"https:\/\/journal-bcs.springeropen.com\/articles\/10.1007\/s13173-012-0072-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,4,17]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2012,11]]}},"alternative-id":["72"],"URL":"https:\/\/doi.org\/10.1007\/s13173-012-0072-8","relation":{},"ISSN":["0104-6500","1678-4804"],"issn-type":[{"value":"0104-6500","type":"print"},{"value":"1678-4804","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,4,17]]},"assertion":[{"value":"15 June 2011","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2012","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 April 2012","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}