{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:39:45Z","timestamp":1742931585636,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031120961"},{"type":"electronic","value":"9783031120978"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-12097-8_7","type":"book-chapter","created":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T16:04:06Z","timestamp":1664294646000},"page":"73-83","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Scalable Adaptive Sampling Based Approach for\u00a0Big Data Classification"],"prefix":"10.1007","author":[{"given":"Kheyreddine","family":"Djouzi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kadda","family":"Beghdad-Bey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdenour","family":"Amamra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,28]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"John, G.H., Langley, P.: In: KDD, vol. 96, pp. 367\u2013370 (1996)","DOI":"10.1111\/j.1571-9979.1996.tb00109.x"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Provost, F., Jensen, D., Oates, T.: Efficient progressive sampling. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 23\u201332. ACM (1999)","DOI":"10.1145\/312129.312188"},{"key":"7_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/11425274_65","volume-title":"Foundations of Intelligent Systems","author":"A Satyanarayana","year":"2005","unstructured":"Satyanarayana, A., Davidson, I.: A dynamic adaptive sampling algorithm (dasa) for real world applications: finger print recognition and face recognition. In: Hacid, M.-S., Murray, N.V., Ra\u015b, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS (LNAI), vol. 3488, pp. 631\u2013640. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11425274_65"},{"issue":"10","key":"7_CR4","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1111\/2041-210X.12581","volume":"7","author":"FL Ratnieks","year":"2016","unstructured":"Ratnieks, F.L., Schrell, F., Sheppard, R.C., Brown, E., Bristow, O.E., Garbuzov, M.: Data reliability in citizen science: learning curve and the effects of training method, volunteer background and experience on identification accuracy of insects visiting ivy flowers. Methods Ecol. Evol. 7(10), 1226\u20131235 (2016)","journal-title":"Methods Ecol. Evol."},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Garg, A., Lee, Y. T., Song, Z., Srivastava, N.: A matrix expander chernoff bound. In Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, pp. 1102\u20131114 (2018)","DOI":"10.1145\/3188745.3188890"},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Satyanarayana, A.: Intelligent sampling for big data using bootstrap sampling and chebyshev inequality. In: 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1\u20136. IEEE (2014)","DOI":"10.1109\/CCECE.2014.6901029"},{"issue":"2","key":"7_CR7","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1080\/00031305.2016.1186559","volume":"71","author":"B Stellato","year":"2017","unstructured":"Stellato, B., Van Parys, B.P., Goulart, P.J.: Multivariate Chebyshev inequality with estimated mean and variance. Am. Stat. 71(2), 123\u2013127 (2017)","journal-title":"Am. Stat."},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/16-SS113","volume":"10","author":"Z Mashreghi","year":"2016","unstructured":"Mashreghi, Z., Haziza, D., L\u00e9ger, C., et al.: A survey of bootstrap methods in finite population sampling. Stat. Surv. 10, 1\u201352 (2016)","journal-title":"Stat. Surv."},{"key":"7_CR9","unstructured":"Meng, X.: Scalable simple random sampling and stratified sampling. In: International Conference on Machine Learning, pp. 531\u2013539 (2013)"},{"issue":"4","key":"7_CR10","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1111\/rssb.12050","volume":"76","author":"A Kleiner","year":"2014","unstructured":"Kleiner, A., Talwalkar, A., Sarkar, P., Jordan, M.I.: A scalable bootstrap for massive data. J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.) 76(4), 795\u2013816 (2014)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Stat. Methodol.)"},{"issue":"7","key":"7_CR11","doi-asserted-by":"publisher","first-page":"3415","DOI":"10.1007\/s11227-018-2391-9","volume":"74","author":"E Gavagsaz","year":"2018","unstructured":"Gavagsaz, E., Rezaee, A., Javadi, H.H.S.: Load balancing in reducers for skewed data in MapReduce systems by using scalable simple random sampling. J. Supercomput. 74(7), 3415\u20133440 (2018)","journal-title":"J. Supercomput."},{"issue":"1","key":"7_CR12","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1177\/0165551516677946","volume":"44","author":"S Xu","year":"2018","unstructured":"Xu, S.: Bayesian na\u00efve bayes classifiers to text classification. J. Inf. Sci. 44(1), 48\u201359 (2018)","journal-title":"J. Inf. Sci."},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Shih, A., Choi, A., Darwiche, A.: A symbolic approach to explaining bayesian network classifiers. arXiv preprint arXiv:1805.03364 (2018)","DOI":"10.24963\/ijcai.2018\/708"},{"issue":"2","key":"7_CR14","first-page":"130","volume":"27","author":"YY Song","year":"2015","unstructured":"Song, Y.Y., Ying, L.: Decision tree methods: applications for classification and prediction. Shanghai Arch. Psychiat. 27(2), 130 (2015)","journal-title":"Shanghai Arch. Psychiat."},{"issue":"1","key":"7_CR15","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s41066-015-0004-z","volume":"1","author":"M Antonelli","year":"2016","unstructured":"Antonelli, M., Ducange, P., Lazzerini, B., Marcelloni, F.: Multi-objective evolutionary design of granular rule-based classifiers. Granular Comput. 1(1), 37\u201358 (2016)","journal-title":"Granular Comput."},{"key":"7_CR16","unstructured":"Howard, A.G.: Some improvements on deep convolutional neural network based image classification. arXiv preprint arXiv:1312.5402 (2013)"},{"key":"7_CR17","unstructured":"Gissin, D., Shalev-Shwartz, S.: Discriminative active learning. arXiv preprint arXiv:1907.06347 (2019)"},{"key":"7_CR18","first-page":"1","volume":"1","author":"D Luengo","year":"2020","unstructured":"Luengo, D., Martino, L., Bugallo, M., Elvira, V., S\u00e4rkk\u00e4, S.: A survey of Monte Carlo methods for parameter estimation. EURASIP J. Adv. Signal Process. 1, 1\u201362 (2020)","journal-title":"EURASIP J. Adv. Signal Process."}],"container-title":["Lecture Notes in Networks and Systems","Advances in Computing Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12097-8_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T16:15:07Z","timestamp":1664295307000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12097-8_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031120961","9783031120978"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12097-8_7","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computing Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Algiers","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Algeria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.emp.mdn.dz\/events\/csa\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}