Ensemble framework for concept-drift detection in multidimensional streaming data

References

• Gama J, Zliobaite I, Bifet A, et al. A survey on concept-drift adaptation. ACM Comput Surv. 2014;46(4):1–4. doi: 10.1145/2523813
   Web of Science ®Google Scholar
• Widmer G, Kubat M. Learning in the presence of concept-drift and hidden contexts. Mach Learn. 1996;23(1):69–101.
   Web of Science ®Google Scholar
• Žliobaitė I. Learning under concept-drift: an overview. arXiv preprint arXiv:1010.4784; 2010.
   Google Scholar
• Hoens TR, Polikar R, Chawla NV. Learning from streaming data with concept-drift and imbalance: an overview. Progress Artif Intell. 2012;1(1):89–101. doi: 10.1007/s13748-011-0008-0
   Google Scholar
• Moreno-Torres JG, Raeder T, Alaiz-RodríGuez R, et al. A unifying view on dataset shift in classification. Pattern Recognit. 2012;45(1):521–530. doi: 10.1016/j.patcog.2011.06.019
   Web of Science ®Google Scholar
• Kolter JZ, Maloof MA. Dynamic weighted majority: an ensemble method for drifting concepts. J Mach Learn Res. 2007;8:2755–2790.
   Web of Science ®Google Scholar
• Hulten G, Spencer L, Domingos P. Mining time-changing data streams. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2001 Aug 26; Washington: ACM. p. 97–106.
   Google Scholar
• Bifet A, Gavalda R. Learning from time-changing data with adaptive windowing. Proceedings of the 2007 SIAM International Conference on Data Mining; 2007 Apr 26; Minneapolis (MN): Society for Industrial and Applied Mathematics. p. 443–448.
   Google Scholar
• Gama J, Medas P, Castillo G, et al. Learning with drift detection. In: Brazilian Symposium on Artificial Intelligence. Berlin: Springer; 2004. p. 286–295.
   Google Scholar
• Baena-García M, del Campo-Ávila J, Fidalgo R, et al. 2006. Early drift detection method. Fourth international workshop on knowledge discovery from data streams. 2006;6:77–86.
   Google Scholar
• Minku LL, Yao X. DDD: a new ensemble approach for dealing with concept-drift. IEEE Trans Knowl Data Eng. 2012;24(4):619–633. doi: 10.1109/TKDE.2011.58
   Web of Science ®Google Scholar
• Bartlett PL, Ben-David S, Kulkarni SR. Learning changing concepts by exploiting the structure of change. Mach Learn. 2000;41(2):153–174. doi: 10.1023/A:1007604202679
   Web of Science ®Google Scholar
• Minku FL, Yao X. Using diversity to handle concept-drift in on-line learning. International Joint Conference on Neural Networks, 2009. IJCNN 2009. 2009 Jun 14; IEEE. p. 2125–2132.
   Google Scholar
• Kosina P, Gama J, Sebastiao R. Drift severity metric. ECAI, Portugal; 2010 Aug 4. p. 1119–1120.
   Google Scholar
• Kullback S, Leibler RA. On information and sufficiency. Annals Math Stat. 1951;22(1):79–86. doi: 10.1214/aoms/1177729694
   Google Scholar
• Hoens TR, Chawla NV, Polikar R. Heuristic updatable weighted random subspaces for non-stationary environments. 2011 11th IEEE International Conference on Data Mining, Canada; 2011 Dec 11; IEEE. p. 241–250.
   Google Scholar
• Tsymbal A. The problem of concept-drift: definitions and related work. Comput Sci Dep Trinity Coll Dublin;2004;106(2):58.
   Google Scholar
• Bose RJ, van der Aalst WM, Žliobaitė I, et al. Handling concept-drift in process mining. International Conference on Advanced Information Systems Engineering; 2011 Jun 20; Berlin: Springer. p. 391–405.
   Google Scholar
• Huang DT, Koh YS, Dobbie G, et al. Tracking drift types in changing data streams. International Conference on Advanced Data Mining and Applications; 2013 Dec 14; Berlin: Springer. p. 72–83.
   Google Scholar
• Minku LL, White AP, Yao X. The impact of diversity on online ensemble learning in the presence of concept-drift. IEEE Trans Knowl Data Eng. 2010;22(5):730–742. doi: 10.1109/TKDE.2009.156
   Web of Science ®Google Scholar
• Dongre PB, Malik LG. A review on real time data stream classification and adapting to various concept-drift scenarios. 2014 IEEE International Advance Computing Conference (IACC), Gurgaon, India; 2014 Feb 21; IEEE. p. 533–537.
   Google Scholar
• Brzeziński D. Mining data streams with concept-drift. Cs. Put. Poznan. Pl. 2010:89.
   Google Scholar
• Bhaduri M, Zhan J, Chiu C, et al. A novel online and non-parametric approach for drift detection in big data. IEEE Access. 2017;5:15883–15892. doi: 10.1109/ACCESS.2017.2735378
   Web of Science ®Google Scholar
• Jadhav A, Deshpande L. An efficient approach to detect concept drifts in data streams. 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad; 2017; IEEE, p. 28–32.
   Google Scholar
• Vallim RM, De Mello RF. Proposal of a new stability concept to detect changes in unsupervised data streams. Expert Syst Appl. 2014;41(16):7350–7360. doi: 10.1016/j.eswa.2014.06.031
   Web of Science ®Google Scholar
• da Costa FG, Duarte FS, Vallim RM, et al. Multidimensional surrogate stability to detect data stream concept drift. Expert Syst Appl. 2017;87:15–29. doi: 10.1016/j.eswa.2017.06.005
   Web of Science ®Google Scholar
• Krawczyk B, Minku LL, Gama J, et al. Ensemble learning for data stream analysis: a survey. Inf Fusion. 2017;37:132–156. doi: 10.1016/j.inffus.2017.02.004
   Web of Science ®Google Scholar
• Almeida PR, Oliveira LS, BrittoJr AS, et al. Adapting dynamic classifier selection for concept drift. Expert Syst Appl. 2018;104:67–85. doi: 10.1016/j.eswa.2018.03.021
   Web of Science ®Google Scholar
• Hu, H, Kantardzic M, Lyu L. Detecting different types of concept drifts with ensemble framework. 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL; 2018 IEEE .
   Google Scholar
• Tao T. Set theory. Analysis I. In: Texts and readings in mathematics, Vol 37. Singapore: Springer; 2016.
   Google Scholar
• Borevich ZI, Shafarevich IR. Number theory. New york: Academic Press; 1986.
   Google Scholar
• Yang XS, Deb S. Engineering optimisation by cuckoo search. arXiv preprint arXiv:1005.2908. 2010.
   Google Scholar
• Stolfo SJ, Fan W, Prodromidis A, et al. Cost-sensitive modelling for fraud and intrusion detection: results from the JAM project. Proceedings of the 2000 DARPA Information Survivability Conference and Exposition, Hilton Head, SC, USA; 2000.
   Google Scholar
• Lippmann RP, Fried DJ, Graf I, et al. Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation. Proceedings 2000 DARPA Information Survivability Conference and Exposition, 2000. DISCEX'00, Hilton Head, SC, USA. IEEE. Vol. 2, p. 12–26.
   Google Scholar
• Available from: https://www.nvidia.com/Download/index.aspx?lang=en-us.
   Google Scholar
• Available from: https://www.oracle.com/technetwork/java/javase/downloads/jdk11-downloads-5066655.html.
   Google Scholar
• Available from: https://github.com/rstudio/rstudio.
   Google Scholar
• Pelleg D, Moore AW. X-means: extending k-means with efficient estimation of the number of clusters. ICML 2000, San Francisco, CA, USA, Jun 29. Vol. 1, p. 727–734.
   Google Scholar