{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:54:35Z","timestamp":1768283675887,"version":"3.49.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319937007","type":"print"},{"value":"9783319937014","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-93701-4_24","type":"book-chapter","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T11:49:52Z","timestamp":1528717792000},"page":"321-333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Combining Data Mining Techniques to Enhance Cardiac Arrhythmia Detection"],"prefix":"10.1007","author":[{"given":"Christian","family":"Gomes","sequence":"first","affiliation":[]},{"given":"Alan","family":"Cardoso","sequence":"additional","affiliation":[]},{"given":"Thiago","family":"Silveira","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Dias","sequence":"additional","affiliation":[]},{"given":"Elisa","family":"Tuler","sequence":"additional","affiliation":[]},{"given":"Renato","family":"Ferreira","sequence":"additional","affiliation":[]},{"given":"Leonardo","family":"Rocha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,6,12]]},"reference":[{"issue":"2","key":"24_CR1","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1145\/276305.276314","volume":"27","author":"Rakesh Agrawal","year":"1998","unstructured":"Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of SIGMOD 1998, pp. 94\u2013105. ACM, New York (1998)","journal-title":"ACM SIGMOD Record"},{"key":"24_CR2","first-page":"110","volume":"29","author":"S Alelyani","year":"2013","unstructured":"Alelyani, S., Tang, J., Liu, H.: Feature selection for clustering: a review. Data Clust.: Algorithms Appl. 29, 110\u2013121 (2013)","journal-title":"Data Clust.: Algorithms Appl."},{"key":"24_CR3","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1214\/09-SS054","volume":"4","author":"S Arlot","year":"2010","unstructured":"Arlot, S., Celisse, A., et al.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40\u201379 (2010)","journal-title":"Stat. Surv."},{"issue":"2","key":"24_CR4","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TKDE.2012.232","volume":"26","author":"S Barua","year":"2014","unstructured":"Barua, S., Islam, M.M., Yao, X., Murase, K.: Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans. Knowl. Data Eng. 26(2), 405\u2013425 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"24_CR5","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/3-540-28349-8_2","volume-title":"Grouping Multidimensional Data","author":"P Berkhin","year":"2006","unstructured":"Berkhin, P.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25\u201371. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/3-540-28349-8_2"},{"issue":"1","key":"24_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"24_CR7","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Int. Res. 16(1), 321\u2013357 (2002). http:\/\/dl.acm.org\/citation.cfm?id=1622407.1622416","DOI":"10.1613\/jair.953"},{"key":"24_CR8","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.eswa.2017.03.073","volume":"82","author":"G Douzas","year":"2017","unstructured":"Douzas, G., Bacao, F.: Self-organizing map oversampling (SOMO) for imbalanced data set learning. Expert Syst. Appl. 82, 40\u201352 (2017)","journal-title":"Expert Syst. Appl."},{"key":"24_CR9","first-page":"138","volume":"22","author":"V Faber","year":"1994","unstructured":"Faber, V.: Clustering and the continuous K-Means algorithm. Los Alamos Sci. 22, 138\u2013144 (1994)","journal-title":"Los Alamos Sci."},{"key":"24_CR10","unstructured":"Farivar, R., Rebolledo, D., Chan, E., Campbell, R.H.: A parallel implementation of K-Means clustering on GPUs. In: Proceedings of PDPTA 2008, USA, pp. 340\u2013345, July 2008"},{"key":"24_CR11","unstructured":"Guvenir, H.A., Acar, B., Demiroz, G., Cekin, A.: A supervised machine learning algorithm for arrhythmia analysis. In: Computers in Cardiology, pp. 433\u2013436. IEEE (1997)"},{"key":"24_CR12","unstructured":"Hall, M.A.: Correlation-based feature subset selection for machine learning. Ph.D. thesis, University of Waikato, Hamilton, New Zealand (1998)"},{"key":"24_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in Intelligent Computing","author":"H Han","year":"2005","unstructured":"Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878\u2013887. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11538059_91"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Jadhav, S.M., Nalbalwar, S., Ghatol, A.: Artificial neural network based cardiac arrhythmia classification using ECG signal data. In: 2010 International Conference on Electronics and Information Engineering (ICEIE), vol. 1, p. V1-228. IEEE (2010)","DOI":"10.1109\/ICEIE.2010.5559887"},{"key":"24_CR15","unstructured":"Joachims, T.: Making large-scale support vector machine learning practical. In: Advances in Kernel Methods, pp. 169\u2013184. MIT Press, Cambridge (1999). http:\/\/dl.acm.org\/citation.cfm?id=299094.299104"},{"key":"24_CR16","unstructured":"Lichman, M.: UCI machine learning repository (2013). https:\/\/archive.ics.uci.edu\/ml\/datasets\/Arrhythmia"},{"key":"24_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5689-3","volume-title":"Feature Selection for Knowledge Discovery and Data Mining","author":"H Liu","year":"2012","unstructured":"Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining, vol. 454. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-1-4615-5689-3"},{"issue":"5","key":"24_CR18","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.compbiomed.2011.03.001","volume":"41","author":"A \u00d6z\u00e7ift","year":"2011","unstructured":"\u00d6z\u00e7ift, A.: Random forests ensemble classifier trained with data resampling strategy to improve cardiac arrhythmia diagnosis. Comput. Biol. Med. 41(5), 265\u2013271 (2011)","journal-title":"Comput. Biol. Med."},{"key":"24_CR19","doi-asserted-by":"crossref","unstructured":"Portela, F., Santos, M.F., Silva, \u00c1., Rua, F., Abelha, A., Machado, J.: Preventing patient cardiac arrhythmias by using data mining techniques. In: 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), pp. 165\u2013170. IEEE (2014)","DOI":"10.1109\/IECBES.2014.7047478"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Salles, T., Gon\u00e7alves, M., Rodrigues, V., Rocha, L.: Broof: exploiting out-of-bag errors, boosting and random forests for effective automated classification. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2015, pp. 353\u2013362. ACM, New York (2015). http:\/\/doi.acm.org\/10.1145\/2766462.2767747","DOI":"10.1145\/2766462.2767747"},{"key":"24_CR21","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.is.2017.04.004","volume":"69","author":"Thiago Salles","year":"2017","unstructured":"Salles, T., Rocha, L., Mour\u00e3o, F., Gon\u00e7alves, M., Viegas, F., Meira, W.: A two-stage machine learning approach for temporally-robust text classification. Inf. Syst. 69(Suppl. C), 40\u201358 (2017). https:\/\/doi.org\/10.1016\/j.is.2017.04.004, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306437917301801","journal-title":"Information Systems"},{"issue":"1","key":"24_CR22","doi-asserted-by":"publisher","first-page":"57","DOI":"10.12720\/ijoee.2.1.57-61","volume":"2","author":"S Samad","year":"2014","unstructured":"Samad, S., Khan, S.A., Haq, A., Riaz, A.: Classification of arrhythmia. Int. J. Electr. Energy 2(1), 57\u201361 (2014)","journal-title":"Int. J. Electr. Energy"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Viegas, F., Gon\u00e7alves, M.A., Martins, W., Rocha, L.: Parallel lazy semi-naive Bayes strategies for effective and efficient document classification. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM 2015, pp. 1071\u20131080. ACM, New York (2015). http:\/\/doi.acm.org\/10.1145\/2806416.2806565","DOI":"10.1145\/2806416.2806565"},{"key":"24_CR24","doi-asserted-by":"publisher","unstructured":"Viegas, F., Rocha, L., Gon\u00e7alves, M., Mour\u00e3o, F., S\u00e1, G., Salles, T., Andrade, G., Sandin, I.: A genetic programming approach for feature selection in highly dimensional skewed data. Neurocomputing (2017). https:\/\/doi.org\/10.1016\/j.neucom.2017.08.050, http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231217314716","DOI":"10.1016\/j.neucom.2017.08.050"},{"key":"24_CR25","unstructured":"Weka: Weka - interface classifier (2016). http:\/\/weka.sourceforge.net\/doc.dev\/weka\/classifiers\/Classifier.html. Accessed 02 Dec 2017"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Wu, J., Xiong, H., Wu, P., Chen, J.: Local decomposition for rare class analysis. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 814\u2013823. ACM (2007)","DOI":"10.1145\/1281192.1281279"},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, M.L., Zhou, Z.H.: A k-nearest neighbor based algorithm for multi-label classification. In: 2005 IEEE International Conference on Granular Computing, pp. 718\u2013721. IEEE (2005)","DOI":"10.1109\/GRC.2005.1547385"},{"key":"24_CR28","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1145\/1007730.1007741","volume":"6","author":"Z Zheng","year":"2004","unstructured":"Zheng, Z., Wu, X., Srihari, R.: Feature selection for text categorization on imbalanced data. ACM SIGKDD Explor. Newsl. 6, 80\u201389 (2004)","journal-title":"ACM SIGKDD Explor. Newsl."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-93701-4_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T02:15:10Z","timestamp":1751681710000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-93701-4_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319937007","9783319937014"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-93701-4_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuxi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 June 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"406","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"148","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}