{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T19:47:05Z","timestamp":1725911225799},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319677910"},{"type":"electronic","value":"9783319677927"}],"license":[{"start":{"date-parts":[[2017,9,20]],"date-time":"2017-09-20T00:00:00Z","timestamp":1505865600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-319-67792-7_30","type":"book-chapter","created":{"date-parts":[[2017,9,18]],"date-time":"2017-09-18T23:25:03Z","timestamp":1505777103000},"page":"301-310","source":"Crossref","is-referenced-by-count":0,"title":["Decision Rule Learning from Stream of Measurements\u2014A Case Study in Methane Hazard Forecasting in Coal Mines"],"prefix":"10.1007","author":[{"given":"Micha\u0142","family":"Kozielski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pawe\u0142","family":"Matyszok","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marek","family":"Sikora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0141ukasz","family":"Wr\u00f3bel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,9,20]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Almeida, E., Kosina, P., Gama, J.: Random rules from data streams. In: SAC 2013, Coimbra, Portugal, pp. 813\u2013814 (2013)","key":"30_CR1","DOI":"10.1145\/2480362.2480518"},{"issue":"3","key":"30_CR2","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1111\/0824-7935.00154","volume":"17","author":"A An","year":"2001","unstructured":"An, A., Cercone, N.: Rule quality measures for rule induction systems: description and evaluation. Comput. Intell. 17(3), 409\u2013424 (2001)","journal-title":"Comput. Intell."},{"key":"30_CR3","first-page":"1601","volume":"11","author":"A Bifet","year":"2010","unstructured":"Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601\u20131604 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"30_CR4","doi-asserted-by":"crossref","first-page":"99","DOI":"10.3233\/IDA-2003-7203","volume":"7","author":"I Bruha","year":"2003","unstructured":"Bruha, I., Tkadlec, J.: Rule quality for multiple-rule classifier: empirical expertise and theoretical methodology. Intell. Data Anal. 7(2), 99\u2013124 (2003)","journal-title":"Intell. Data Anal."},{"issue":"8","key":"30_CR5","first-page":"1426","volume":"11","author":"FJ Ferrer-Troyano","year":"2005","unstructured":"Ferrer-Troyano, F.J., Aguilar-Ruiz, J.S., Santos, J.C.R.: Incremental rule learning and border examples selection from numerical data streams. J. Univ. Comput. Sci. 11(8), 1426\u20131439 (2005)","journal-title":"J. Univ. Comput. Sci."},{"unstructured":"Gama, J., Kosina, P.: Learning decision rules from data streams. In: IJCAI 2011, Barcelona, Spain, pp. 1255\u20131260 (2011)","key":"30_CR6"},{"issue":"3","key":"30_CR7","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1145\/1132960.1132963","volume":"38","author":"L Geng","year":"2006","unstructured":"Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 9 (2006)","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"30_CR8","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1007\/s10618-010-0201-y","volume":"23","author":"E Ikonomovska","year":"2011","unstructured":"Ikonomovska, E., Gama, J., D\u017eeroski, S.: Learning model trees from evolving data streams. Data Min. Knowl. Disc. 23(1), 128\u2013168 (2011)","journal-title":"Data Min. Knowl. Disc."},{"key":"30_CR9","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10994-009-5162-2","volume":"78","author":"F Janssen","year":"2010","unstructured":"Janssen, F., F\u00fcrnkranz, J.: On the quest for optimal rule learning heuristics. Mach. Learn. 78, 343\u2013379 (2010)","journal-title":"Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Kosina, P., Gama, J.: Very fast decision rules for multi-class problems. In: SAC 2012, Trento, Italy, pp. 795\u2013800 (2012)","key":"30_CR10","DOI":"10.1145\/2245276.2245431"},{"issue":"1","key":"30_CR11","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1007\/s10618-013-0340-z","volume":"29","author":"P Kosina","year":"2015","unstructured":"Kosina, P., Gama, J.: Very fast decision rules for classification in data streams. Data Min. Knowl. Disc. 29(1), 168\u2013202 (2015)","journal-title":"Data Min. Knowl. Disc."},{"issue":"2","key":"30_CR12","doi-asserted-by":"crossref","first-page":"218","DOI":"10.17531\/ein.2016.2.9","volume":"18","author":"M Kozielski","year":"2016","unstructured":"Kozielski, M., Sikora, M., Wr\u00f3bel, \u0141.: Decision support and maintenance system for natural hazards, processes and equipment monitoring. Eksploatacja i Niezawodno\u015b\u0107-Maint. Reliab. 18(2), 218\u2013228 (2016)","journal-title":"Eksploatacja i Niezawodno\u015b\u0107-Maint. Reliab."},{"issue":"1","key":"30_CR13","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.artint.2003.04.001","volume":"154","author":"MA Maloof","year":"2004","unstructured":"Maloof, M.A., Michalski, R.S.: Incremental learning with partial instance memory. Artif. Intell. 154(1), 95\u2013126 (2004)","journal-title":"Artif. Intell."},{"issue":"3","key":"30_CR14","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s10115-014-0808-1","volume":"45","author":"HL Nguyen","year":"2015","unstructured":"Nguyen, H.L., Woon, Y.K., Ng, W.K.: A survey on data stream clustering and classification. Knowl. Inf. Syst. 45(3), 535\u2013569 (2015)","journal-title":"Knowl. Inf. Syst."},{"issue":"6","key":"30_CR15","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1109\/TKDE.2012.66","volume":"25","author":"L Rutkowski","year":"2013","unstructured":"Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid\u2019s bound. IEEE Trans. Knowl. Data Eng. 25(6), 1272\u20131279 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"30_CR16","first-page":"317","volume":"1","author":"JC Schlimmer","year":"1986","unstructured":"Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Mach. Learn. 1(3), 317\u2013354 (1986)","journal-title":"Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Sikora, M., Wr\u00f3bel, \u0141.: Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm. In: Kuznetsov, S.O., \u015al\u0229zak, D., Hepting, D.H., Mirkin, B.G. (eds.) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: 13th International Conference, RSFDGrC 2011, Moscow, Russia, 25\u201327 June 2011. LNCS, vol. 6743, pp. 278\u2013285. Springer, Heidelberg (2011)","key":"30_CR17","DOI":"10.1007\/978-3-642-21881-1_44"},{"issue":"6","key":"30_CR18","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1080\/03081079.2013.798901","volume":"42","author":"M Sikora","year":"2013","unstructured":"Sikora, M., Wr\u00f3bel, L.: Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms. Int. J. Gen Syst 42(6), 594\u2013613 (2013)","journal-title":"Int. J. Gen Syst"},{"issue":"1","key":"30_CR19","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ijar.2004.11.004","volume":"40","author":"D Slezak","year":"2005","unstructured":"Slezak, D., Ziarko, W.: The investigation of the Bayesian rough set model. Int. J. Approx. Reason. 40(1), 81\u201391 (2005)","journal-title":"Int. J. Approx. Reason."},{"issue":"1","key":"30_CR20","first-page":"69","volume":"23","author":"G Widmer","year":"1996","unstructured":"Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69\u2013101 (1996)","journal-title":"Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Xiong, H., Shekhar, S., Tan, P.N., Kumar, V.: Exploiting a support-based upper bound of pearson\u2019s correlation coefficient for efficiently identifying strongly correlated pairs. In: SIGKDD 2004, Seattle, USA, pp. 334\u2013343 (2004)","key":"30_CR21","DOI":"10.1145\/1014052.1014090"}],"container-title":["Advances in Intelligent Systems and Computing","Man-Machine Interactions 5"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-67792-7_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,3]],"date-time":"2019-10-03T13:02:25Z","timestamp":1570107745000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-67792-7_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,20]]},"ISBN":["9783319677910","9783319677927"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-67792-7_30","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2017,9,20]]}}}