{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:20:20Z","timestamp":1770747620932,"version":"3.49.0"},"reference-count":34,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2016,12,23]],"date-time":"2016-12-23T00:00:00Z","timestamp":1482451200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,1,30]]},"abstract":"<jats:p>The paper proposes two variants of the ensemble distance-based and Naive-Bayes online classifiers with data reduction. In the first variant the reduced dataset is obtained through applying bias-correction fuzzy clustering. In the second we used the kernel-based fuzzy clustering as the data reduction tool. It is assumed that vectors of data with unknown class label arrive one by one, and that there is available an initial chunk of data with known class labels serving as the initial training set. Classification is carried-out in rounds. Each round involves a number of the classification decisions equal to the chunk size. For each round a set of base classifiers is constructed using different distance metrics. Set of base classifiers is extended with the Naive-Bayes classifier. The unknown label of each incoming vector is determined through weighted majority voting. After each round has been completed the training set is replaced by the fresh one and the classification process is continued. The approach is validated through computational experiment involving a number of datasets often used for testing data streams mining algorithms.<\/jats:p>","DOI":"10.3233\/jifs-169127","type":"journal-article","created":{"date-parts":[[2016,12,23]],"date-time":"2016-12-23T15:59:48Z","timestamp":1482508788000},"page":"1289-1296","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["An ensemble of the distance-based and Naive Bayes classifiers for the online classification with data reduction"],"prefix":"10.1177","volume":"32","author":[{"given":"Joanna","family":"J\u0119drzejowicz","sequence":"first","affiliation":[{"name":"Institute of Informatics, Faculty of Mathematics, Physics and Informatics, University of Gdansk, Gdansk, Poland"}]},{"given":"Piotr","family":"J\u0119drzejowicz","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Gdynia Maritime University, Gdynia, Poland"}]}],"member":"179","published-online":{"date-parts":[[2016,12,23]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Waikato machine learning repository 2013."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587598"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2003.814839"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00354-008-0073-5"},{"key":"e_1_3_1_7_2","first-page":"759","volume-title":"Data Mining and Knowledge Discovery Handbook","author":"Gaber M.M.","year":"2010","unstructured":"GaberM.M., ZaslavskyA.B. and KrishnaswamyS., Data stream mining, In Data Mining and Knowledge Discovery Handbook, Springer-Verlag, 2010, pp. 759\u2013787."},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-73679-4"},{"key":"e_1_3_1_9_2","author":"Garc\u00eda S.","year":"2015","unstructured":"Garc\u00edaS., LuengoJ. and HerreraF., Data Preprocessing in Data Mining, volume 72 of Intelligent Systems Reference Library. Springer, 2015.","journal-title":"Data Preprocessing in Data Mining, volume 72 of Intelligent Systems Reference Library"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2009.10.021"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40495-5_43"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-05458-2_19"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-19857-6_25"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.05.016"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2002-6203"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.441658"},{"key":"e_1_3_1_17_2","unstructured":"LichmanM. UCI machine learning repository 2013."},{"key":"e_1_3_1_18_2","volume-title":"Machine Learning for Adaptive Many-Core Machines - A Practical Approach","author":"Lopes N.","year":"2014","unstructured":"LopesN. and RibeiroB., Machine Learning for Adaptive Many-Core Machines - A Practical Approach, Springer International Publishing, 2014."},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.83"},{"key":"e_1_3_1_20_2","unstructured":"Mldata.org. Machine learning data set repository 2013."},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2199516"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(02)00060-6"},{"issue":"11","key":"e_1_3_1_23_2","first-page":"26","article-title":"Data stream mining: A review on windowing approach","volume":"12","author":"Pramod S.","year":"2012","unstructured":"PramodS. and VyasO.P., Data stream mining: A review on windowing approach, Global Journal of Computer Science and Technology Software & Data Engineering12(11) (2012), 26\u201330.","journal-title":"Global Journal of Computer Science and Technology Software & Data Engineering"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2008.11.006"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.12.041"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-45062-4_26"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.06.023"},{"key":"e_1_3_1_28_2","unstructured":"WebbA.R. Statistical pattern recognition. John Wiley & Sons Ltd. cop. West Sussex England Hoboken NJ 2002. R\u00c3l\u2019impression 2003."},{"key":"e_1_3_1_29_2","first-page":"207","article-title":"Distance metric learning for large margin nearest neighbor classification","volume":"10","author":"Weinberger K.Q.","year":"2009","unstructured":"WeinbergerK.Q. and SaulL.K., Distance metric learning for large margin nearest neighbor classification, Journal of Machine Learning Research10 (2009), 207\u2013244.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.346"},{"issue":"4","key":"e_1_3_1_31_2","first-page":"116","article-title":"A comparison of different classification techniques for bank direct marketing","volume":"3","author":"Wisaeng K.","year":"2013","unstructured":"WisaengK., A comparison of different classification techniques for bank direct marketing, International Journal of Soft Computing and Engineering3(4) (2013), 116\u2013119.","journal-title":"International Journal of Soft Computing and Engineering"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2015.03.006"},{"key":"e_1_3_1_33_2","first-page":"162","volume-title":"Proceedings of International Conference on Control and Automation ICCA","author":"Zhang D.","year":"2002","unstructured":"ZhangD. and ChenS., Fuzzy clustering using kernel method. In Proceedings of International Conference on Control and Automation ICCA, Xiamen, China, 2002, pp. 162\u2013163."},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:NEPL.0000011135.19145.1b"},{"key":"e_1_3_1_35_2","first-page":"365","volume-title":"Discovery Science - 14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings, volume 6926 of Lecture Notes in Computer Science","author":"Zliobaite I.","year":"2011","unstructured":"ZliobaiteI., Controlled permutations for testing adaptive classifiers. In ElomaaTapio, Hollm\u00e9nJaakko and MannilaHeikki, editors, Discovery Science - 14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings, volume 6926 of Lecture Notes in Computer Science, Springer, 2011, pp. 365\u2013379."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169127","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169127","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169127","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T02:19:14Z","timestamp":1770517154000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,23]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2017,1,30]]}},"alternative-id":["10.3233\/JIFS-169127"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169127","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,23]]}}}