This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Bach, S. H. and Maloof, M. A. 2008. “Paired learners for concept drift.” Eighth IEEE International Conference on Data Mining. IEEE.BachS. H. and MaloofM. A.2008“Paired learners for concept drift.” Eighth IEEE International Conference on Data Mining. IEEESearch in Google Scholar
Baena-Garcıa, M., del Campo-Ávila, J., Fidalgo, R., Bifet, A., Gavalda, R. and Morales-Bueno, R. 2006. “Early drift detection method”. Fourth International Workshop on Knowledge Discovery from Data Streams 6: 77–86.Baena-GarcıaM.del Campo-ÁvilaJ.FidalgoR.BifetA.GavaldaR. and Morales-BuenoR.2006“Early drift detection method”Fourth International Workshop on Knowledge Discovery from Data Streams677–86Search in Google Scholar
Bifet, A. 2009. “Adaptive Learning and Mining for Data Streams and Frequent Patterns”, Doctoral Thesis.BifetA.2009“Adaptive Learning and Mining for Data Streams and Frequent Patterns”, Doctoral ThesisSearch in Google Scholar
Bifet, A. and Gavalda, R. 2007. “Learning from time-changing data with adaptive windowing.” Proceedings of the 2007 SIAM international conference on data mining. Society for Industrial and Applied Mathematics.BifetA. and GavaldaR.2007“Learning from time-changing data with adaptive windowing.” Proceedings of the 2007 SIAM international conference on data mining. Society for Industrial and Applied MathematicsSearch in Google Scholar
Bifet, A., et al. 2009. “New ensemble methods for evolving data streams.” Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM.BifetA.2009“New ensemble methods for evolving data streams.” Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACMSearch in Google Scholar
Brzeziński, D. and Stefanowski, J. 2011. “Accuracy Updated Ensemble for Data Streams with Concept Drift.” International Conference on Hybrid Artificial Intelligence Systems Springer, Berlin and Heidelberg.BrzezińskiD. and StefanowskiJ.2011“Accuracy Updated Ensemble for Data Streams with Concept Drift.” International Conference on Hybrid Artificial Intelligence SystemsSpringerBerlin and HeidelbergSearch in Google Scholar
Brzezinski, D. and Stefanowski, J. 2012. “From block-based ensembles to online learners in changing data streams: If-and how-to.” Proceedings of the 2012 ECML PKDD Workshop on Instant Interactive Data Mining, Available at: http://adrem.ua.ac.be/iid2012.BrzezinskiD. and StefanowskiJ.2012“From block-based ensembles to online learners in changing data streams: If-and how-to.” Proceedings of the 2012 ECML PKDD Workshop on Instant Interactive Data Mining, Available at:http://adrem.ua.ac.be/iid2012.Search in Google Scholar
Brzezinski, D. and Stefanowski, J. 2014a. Reacting to different types of concept drift: The accuracy updated ensemble algorithm. Neural Networks and Learning Systems, IEEE Transactions on 25(1): 81–94, doi: 10.1109/TNNLS.2013.2251352.BrzezinskiD. and StefanowskiJ.2014aReacting to different types of concept drift: The accuracy updated ensemble algorithmNeural Networks and Learning Systems, IEEE Transactions on25(1):81–94,doi:10.1109/TNNLS.2013.2251352Open DOISearch in Google Scholar
Brzezinski, D. and Stefanowski, J. 2014b. “Combining block-based and online methods in learning ensembles from concept drifting data streams”. An International Journal: Information Sciences 265: 50–67.BrzezinskiD. and StefanowskiJ.2014b“Combining block-based and online methods in learning ensembles from concept drifting data streams”An International Journal: Information Sciences26550–67Search in Google Scholar
Budiman, A., Fanany, M. I. and Basaruddin, C. 2016. Adaptive Online Sequential ELM for Concept Drift Tackling. Computational Intelligence and Neuroscience 2016(20): 17, Available at: https://doi.org/10.1155/2016/8091267.BudimanA.FananyM. I. and BasaruddinC.2016Adaptive Online Sequential ELM for Concept Drift TacklingComputational Intelligence and Neuroscience2016(20):17, Available at:https://doi.org/10.1155/2016/8091267Search in Google Scholar
Budiman, A., Fanany, M. I. and Basaruddin, C. 2017. Adaptive Parallel ELM with Convolutional Features for Big Stream Data. Thesis Dissertation, Faculty of Computer Science, University of Indonesia, doi: 10.13140/RG.2.2.18500.22404.BudimanA.FananyM. I. and BasaruddinC.2017Adaptive Parallel ELM with Convolutional Features for Big Stream DataThesis Dissertation, Faculty of Computer Science, University of Indonesia, doi: 10.13140/RG.2.2.18500.22404Search in Google Scholar
Cao, K., Wang, G., Han, D., Ning, J. and Zhang, X. 2015. Classification of Uncertain Data Streams Based on Extreme Learning Machine. Cognitive Computation 7(1): 150–160.CaoK.WangG.HanD.NingJ. and ZhangX.2015Classification of Uncertain Data Streams Based on Extreme Learning MachineCognitive Computation7(1):150–160Search in Google Scholar
Dariusz, B. 2010. Mining data streams with concept drift. Master’s thesis, Poznan University of Technology.DariuszB.2010Mining data streams with concept drift. Master’s thesis, Poznan University of TechnologySearch in Google Scholar
Demšar, J. and Bosnić, Z. 2018. Detecting concept drift in data streams using model explanation. Expert Systems with Applications 92: 546–559.DemšarJ. and BosnićZ.2018Detecting concept drift in data streams using model explanationExpert Systems with Applications92546–559Search in Google Scholar
Ditzler, G. and Polikar, R. 2013. Incremental learning of Concept Drift from Streaming Imbalanced Data. IEEE Trans. Knowledge Data Engineering 25(10): 2283–2301.DitzlerG. and PolikarR.2013Incremental learning of Concept Drift from Streaming Imbalanced DataIEEE Trans. Knowledge Data Engineering25(10):2283–2301Search in Google Scholar
Dongre, P. B. and Malik, L. G. 2014. A review on real time data stream classification and adapting to various concept drift scenarios. In Advance Computing Conference (IACC), 2014 IEEE International, February, pp. 533–537, doi: 10.1109/IAdCC.2014.6779381.DongreP. B. and MalikL. G.2014A review on real time data stream classification and adapting to various concept drift scenarios.In Advance Computing Conference (IACC), 2014 IEEE International, February, pp.533–537doi:10.1109/IAdCC.2014.6779381Open DOISearch in Google Scholar
Dyer, K. B. and Polikar, R. 2012. “Semi-supervised learning in initially labeled nonstationary environments with gradual drift.” The International Joint Conference on Neural Networks (IJCNN). IEEE.DyerK. B. and PolikarR.2012“Semi-supervised learning in initially labeled nonstationary environments with gradual drift.” The International Joint Conference on Neural Networks (IJCNN). IEEESearch in Google Scholar
Freund, Y. and Schapire, R. E. 1997. A decision-theoretic generalization of online learning and an application to boosting. Journal of Computer and System Sciences 55(1): 119–139.FreundY. and SchapireR. E.1997A decision-theoretic generalization of online learning and an application to boostingJournal of Computer and System Sciences55(1):119–139Search in Google Scholar
Friedman, J. H. and Rafsky, L. C. 1979. “Multivariate generalizations of the wald-wolfowitz and smirnov two-sample tests”. Institute of Mathematical Statistics, 7(4): 697–717, doi: 10.1214/aos/1176344722.FriedmanJ. H. and RafskyL. C.1979. “Multivariate generalizations of the wald-wolfowitz and smirnov two-sample tests”.Institute of Mathematical Statistics7(4):697–717, doi:10.1214/aos/1176344722Open DOISearch in Google Scholar
Gama, J., Medas, P., Castillo, G. and Rodrigues, P. 2004. Learning with drift detection. In Advances in Artificial Intelligence–SBIA, Springer Berlin and Heidelberg, pp. 286–295.GamaJ.MedasP.CastilloG. and RodriguesP.2004Learning with drift detectionIn Advances in Artificial Intelligence–SBIASpringer Berlin and Heidelberg, pp.286–295Search in Google Scholar
Ghorbani, S., Barari, M. and Hosseini, M. 2017. “A modern method to improve of detecting and categorizing mechanism for micro seismic events data using boost learning system”. Civil Engineering Journal 3(9): 715–726.GhorbaniS.BarariM. and HosseiniM.2017“A modern method to improve of detecting and categorizing mechanism for micro seismic events data using boost learning system”Civil Engineering Journal3(9):715–726Search in Google Scholar
Gomes, J. B., Menasalvas, E. and Sousa, P. A. C. 2011. “Learning recurring concepts from data streams with a context-aware ensemble”, Proceedings of the 2011 ACM Symposium on Applied Computing, SAC ‘11 ACM, New York, NY, pp. 994–999, doi: 10.1145/1982185.1982403.GomesJ. B.MenasalvasE. and SousaP. A. C.2011. “Learning recurring concepts from data streams with a context-aware ensemble”,Proceedings of the 2011 ACM Symposium on Applied Computing, SAC ‘11ACMNew York, NY, pp.994–999doi:10.1145/1982185.1982403Open DOISearch in Google Scholar
Gupta, B. M. and Dhawan, S. M. 2019. Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004-17. Emerging Science Journal 3(1): 23–32.GuptaB. M. and DhawanS. M.2019Deep Learning Research: Scientometric Assessment of Global Publications Output during 2004-17Emerging Science Journal3(1):23–32Search in Google Scholar
Harel, M., et al. 2014. Concept drift detection through resampling. International Conference on Machine Learning.HarelM.2014Concept drift detection through resamplingInternational Conference on Machine LearningSearch in Google Scholar
Hoens, T. R., Chawla, N. V. and Polikar, R. 2011. “Heuristic updatable weighted random subspaces for nonstationary environments”, In Cook, D. J., Pei, J. W., Wei, Z., Osmar, R. and Wu, X. (Eds), IEEE International Conference on Data Mining, ICDM-11, IEEE, pp. 241–250.HoensT. R.ChawlaN. V. and PolikarR.2011. “Heuristic updatable weighted random subspaces for nonstationary environments”, InCookD. J.PeiJ.W.WeiZ.Osmar, R. and WuX. (Eds)IEEE International Conference on Data Mining, ICDM-11IEEE, pp.241–250Search in Google Scholar
Hoens, T. R., Polikar, R. and Chawla, N. V. 2012. Learning from streaming data with concept drift and imbalance: an overview. Progress in Artificial Intelligence 1(1): 89–101, doi: 10.1007/s13748-011-0008-0.HoensT. R.PolikarR. and ChawlaN. V.2012Learning from streaming data with concept drift and imbalance: an overviewProgress in Artificial Intelligence1(1):89–101doi: 10.1007/s13748-011-0008-0Search in Google Scholar
Huang, D. T. J., Koh, Y. S., Dobbie, G. and Pears, R. 2013. “Tracking drift types in changing data streams”, In Hiroshi, M., Wu, Z., Cao, L., Zaiane, O., Yao, M. and Wang, W. (Eds), Advanced Data Mining and Applications, volume 8346 of Lecture Notes in Computer Science, Springer, Berlin and Heidelberg, pp. 72–83, doi: 10.1007/978-3-642-53914-57.HuangD. T. J.KohY. S.DobbieG. and PearsR.2013. “Tracking drift types in changing data streams”, InHiroshiM.WuZ.CaoL.ZaianeO.YaoM. and WangW. (Eds)Advanced Data Mining and Applications, volume 8346 of Lecture Notes in Computer ScienceSpringerBerlin and Heidelberg, pp.72–83, doi:10.1007/978-3-642-53914-57Open DOISearch in Google Scholar
Huang, G. B. 2006. Extreme Learning Machine. Theory and Applications. Neuro Computing 70(1–3): 489–501.HuangG. B.2006Extreme Learning Machine. Theory and ApplicationsNeuro Computing70(1–3):489–501Search in Google Scholar
Huang, G. B., Zhou, H., Ding, X. and Zhang, R. 2012. Extreme Learning Machine for Regression and Multiclass Classification. IEEE Transactions on Systems, Man, and Cybernetics 42(2): 513–529.HuangG. B.ZhouH.DingX. and ZhangR.2012Extreme Learning Machine for Regression and Multiclass ClassificationIEEE Transactions on Systems, Man, and Cybernetics42(2):513–529Search in Google Scholar
Iwashita, A., Sayuri and Papa, J. P. 2019. “An Overview on Concept Drift Learning”. IEEE Access 7: 1532–1547.IwashitaA.Sayuri and PapaJ. P.2019“An Overview on Concept Drift Learning”IEEE Access71532–1547Search in Google Scholar
Jagadeesh Chandra Bose, R. P., van der Aalst, W. M. P., Zliobaite, I. and Pechenizkiy, M. 2011. “Handling concept drift in process mining”, In Haralambos, M. and Colette, R. (Eds), Advanced Information Systems Engineering, volume 6741 of Lecture Notes in Computer Science, Springer, Berlin and Heidelberg, pp. 391–405, doi: 10.1007/978-3-642-21640-430.Jagadeesh Chandra BoseR. P.van der AalstW. M. P.ZliobaiteI. and PechenizkiyM.2011. “Handling concept drift in process mining”, InHaralambosM. and ColetteR. (Eds)Advanced Information Systems Engineering, volume 6741 of Lecture Notes in Computer ScienceSpringerBerlin and Heidelberg, pp.391–405, doi:10.1007/978-3-642-21640-430Open DOISearch in Google Scholar
Jameel, S. M., et al. 2018. “A Fully Adaptive Image Classification Approach for Industrial Revolution 4.0.” International Conference of Reliable Information and Communication Technology Springer, Cham.JameelS. M.2018“A Fully Adaptive Image Classification Approach for Industrial Revolution 4.0.” International Conference of Reliable Information and Communication TechnologySpringerChamSearch in Google Scholar
Jameel, S. M., Hashmani, M. A., Rehman, M. and Budiman, A. 2020a. An Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things Environment. Sensors 20(20): 5811, doi: 10.3390/s20205811.JameelS. M.HashmaniM. A.RehmanM. and BudimanA.2020aAn Adaptive Deep Learning Framework for Dynamic Image Classification in the Internet of Things EnvironmentSensors20(20):5811, doi:10.3390/s20205811Search in Google Scholar
Jameel, S. M., Hashmani, M. A., Rehman, M. and Budiman, A. 2020b. Adaptive CNN Ensemble for Complex Multispectral Image Analysis. Complexity 2020: 21, Available at: https://doi.org/10.1155/2020/8361989.JameelS. M.HashmaniM. A.RehmanM. and BudimanA.2020bAdaptive CNN Ensemble for Complex Multispectral Image AnalysisComplexity2020: 21, Available at:https://doi.org/10.1155/2020/8361989Search in Google Scholar
Jameel, S. M., Hashmani, M. A., Alhussain, H., Rehman, M. and Budiman, A. 2020c. “A Critical Review on Adverse Effects of Concept Drift over Machine Learning Classification Models”. International Journal of Advanced Computer Science and Applications (IJACSA) 11(1): 2020, Available at: http://dx.doi.org/10.14569/IJACSA.2020.0110127.JameelS. M.HashmaniM. A.AlhussainH.RehmanM. and BudimanA.2020c“A Critical Review on Adverse Effects of Concept Drift over Machine Learning Classification Models”International Journal of Advanced Computer Science and Applications (IJACSA)11(1): 2020, Available at:http://dx.doi.org/10.14569/IJACSA.2020.0110127Search in Google Scholar
Jensen, C., et al. 2019. “Piloting a Methodology for Sustainability Education: Project Examples and Exploratory Action Research Highlights”. Emerging Science Journal 3(5): 312–326.JensenC.2019“Piloting a Methodology for Sustainability Education: Project Examples and Exploratory Action Research Highlights”Emerging Science Journal3(5):312–326Search in Google Scholar
Kearns and Vazirani. 1994. PAC learning model.Kearns and Vazirani1994. PAC learning model.Search in Google Scholar
Khamassi, I., Sayed-Mouchaweh, M. and Hammami, M. 2015. Self-Adaptive Windowing Approach for Handling Complex Concept Drift. Cognitive Computing 7(6): 772–790.KhamassiI.Sayed-MouchawehM. and HammamiM.2015Self-Adaptive Windowing Approach for Handling Complex Concept DriftCognitive Computing7(6):772–790Search in Google Scholar
Khamassi, I., et al., 2019. “A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift”. Learning from Data Streams in Evolving Environments Springer, Cham, pp. 39–61.KhamassiI.2019“A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Drift”. Learning from Data Streams in Evolving EnvironmentsSpringerCham, pp.39–61Search in Google Scholar
Kifer, D., Ben-David, S. and Gehrke, J. 2004. Detecting change in data streams. In Proceedings of the International Conference on Very Large Data Bases, Toronto, Canada, Morgan Kaufmann, pp. 180–191.KiferD.Ben-DavidS. and GehrkeJ.2004Detecting change in data streams.In Proceedings of the International Conference on Very Large Data Bases, Toronto, Canada, Morgan Kaufmann, pp.180–191Search in Google Scholar
Kitchenham, B. 2004. “Procedures for performing systematic reviews,” Department of Computer Science, Keele University, ST5 5BG, U.K., Tech. Rep. TR/SE-0401.KitchenhamB.2004“Procedures for performing systematic reviews,” Department of Computer Science, Keele University, ST5 5BG, U.K., Tech. Rep. TR/SE-0401Search in Google Scholar
Kitchenham, B. A. and Charters, S. 2007. Guidelines for performing systematic literature reviews in software engineering, Tech. Rep. EBSE-2007-01, Keele University and University of Durham.KitchenhamB. A. and ChartersS.2007Guidelines for performing systematic literature reviews in software engineering, Tech. Rep. EBSE-2007-01, Keele University and University of DurhamSearch in Google Scholar
Krawczyk, B. 2015. Reacting to Different Types of Concept Drift One Class Classifiers. 2nd International Conference on Cybernetics, IEEE, Gdynia, Poland, pp. 30–35.KrawczykB.2015Reacting to Different Types of Concept Drift One Class Classifiers.2nd International Conference on Cybernetics, IEEE, Gdynia,Poland,pp.30–35Search in Google Scholar
Kuncheva, L. I. 2004. “Classifier Ensembles for Changing Environments”, In Roli, F., Kittler, J. and Windeatt, T. (Eds), Multiple Classifier Systems. MCS. LNCS 3077, Springer, Berlin and Heidelberg, pp. 1–15.KunchevaL. I.2004. “Classifier Ensembles for Changing Environments”, InRoliF.KittlerJ. and WindeattT.(Eds),Multiple Classifier Systems. MCS. LNCS3077SpringerBerlin and Heidelberg, pp.1–15Search in Google Scholar
Lan, Y., Soh, Y. C. and Huang, G. 2009. “A constructive enhancement for Online Sequential Extreme Learning Machine,” 2009 International Joint Conference on Neural Networks, Atlanta, GA, pp. 1708–1713, doi: 10.1109/IJCNN.2009.5178608.LanY.SohY. C. and HuangG.2009. “A constructive enhancement for Online Sequential Extreme Learning Machine,”2009 International Joint Conference on Neural Networks, Atlanta, GA, pp.1708–1713, doi:10.1109/IJCNN.2009.5178608Search in Google Scholar
Lavaire, J. D. D., et al. 2015. “Dimensional scalability of supervised and unsupervised concept drift detection: An empirical study.” 2015 IEEE International Conference on Big Data (Big Data). IEEE.LavaireJ. D. D.2015“Dimensional scalability of supervised and unsupervised concept drift detection: An empirical study.” 2015 IEEE International Conference on Big Data (Big Data). IEEESearch in Google Scholar
Liang, N., Huang, G., Saratchandran, P. and Sundararajan, N. 2006. A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks. IEEE Transactions Neural Networks 17(6): 1411–1423.LiangN.HuangG.SaratchandranP. and SundararajanN.2006A Fast and Accurate Online Sequential Learning Algorithm for Feedforward NetworksIEEE Transactions Neural Networks17(6):1411–1423Search in Google Scholar
Liu, N. and Wang, H. 2010. Ensemble based Extreme Learning Machine. IEEE. Signal Process 17(8): 754–757.LiuN. and WangH.2010Ensemble based Extreme Learning Machine. IEEESignal Process17(8):754–757Search in Google Scholar
Liu, Z., Loo, C. K. and Seera, M. 2019. “Meta-cognitive Recurrent Recursive Kernel OS-ELM for concept drift handling”. Applied Soft Computing 75: 494–507.LiuZ.LooC. K. and SeeraM.2019“Meta-cognitive Recurrent Recursive Kernel OS-ELM for concept drift handling”Applied Soft Computing75494–507Search in Google Scholar
Mehta, S. 2017. Concept drift in Streaming Data Classification: Algorithms, Platforms, and Issues. Procedia computer science 122: 804–811.MehtaS.2017Concept drift in Streaming Data Classification: Algorithms, Platforms, and IssuesProcedia computer science122804–811Search in Google Scholar
Minku, L. L., White, A. P. and Yao, X. May 2010. The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Transactions on Knowledge and Data Engineering 22(5): 730–742, doi: 10.1109/TKDE.2009.156.MinkuL. L.WhiteA. P. and YaoX.May 2010The impact of diversity on online ensemble learning in the presence of concept driftIEEE Transactions on Knowledge and Data Engineering22(5):730–742, doi:10.1109/TKDE.2009.156Open DOISearch in Google Scholar
Mouss, H., Mouss, D., Mouss, N. and Sefouhi, L. 2004. Test of Page-Hinkley, an Approach for Fault Detection in an Agro-Alimentary Production System. Proceedings of the 5th Asian Control Conference 2: 815–818.MoussH.MoussD.MoussN. and SefouhiL.2004Test of Page-Hinkley, an Approach for Fault Detection in an Agro-Alimentary Production SystemProceedings of the 5th Asian Control Conference2815–818Search in Google Scholar
Nishida, K. 2008. “Learning and Detecting Concept Drift”, A Dissertation: Doctor of Philosophy in Information Science and Technology, Graduate School of Information Science and Technology, Hokkaido University].NishidaK.2008“Learning and Detecting Concept Drift”, A Dissertation: Doctor of Philosophy in Information Science and Technology, Graduate School of Information Science and Technology, Hokkaido University]Search in Google Scholar
Nishida, K., et al. 2008. “Detecting sudden concept drift with knowledge of human behavior.” 2008 IEEE International Conference on Systems, Man and Cybernetics. IEEE.NishidaK.2008“Detecting sudden concept drift with knowledge of human behavior.” 2008 IEEE International Conference on Systems, Man and Cybernetics. IEEESearch in Google Scholar
Page, E. S. 1954. Continuous Inspection Schemes. Biometrika 41: 100–115.PageE. S.1954Continuous Inspection SchemesBiometrika41100–115Search in Google Scholar
Petersen, K., Feldt, R., Mujtaba, S. and Mattsson, M. 2008. “Systematic mapping studies in software engineering,” in Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE 2008).PetersenK.FeldtR.MujtabaS. and MattssonM.2008“Systematic mapping studies in software engineering,” in Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE 2008)Search in Google Scholar
Pfleeger, S. L. 2005. Soup or art? The role of evidential force in empirical software engineering. IEEE Software 22(1): 66–73.PfleegerS. L.2005Soup or art? The role of evidential force in empirical software engineeringIEEE Software22(1):66–73Search in Google Scholar
Raza, H., Prasad, G. and Li, Y. 2014. “Adaptive learning with covariate shift-detection for nonstationary environments.” 2014 14th U.K. Workshop on Computational Intelligence (UKCI). IEEE.RazaH.PrasadG. and LiY.2014“Adaptive learning with covariate shift-detection for nonstationary environments.” 2014 14th U.K. Workshop on Computational Intelligence (UKCI). IEEESearch in Google Scholar
Ross, G. J., et al. 2012. “Exponentially weighted moving average charts for detecting concept drift”. Pattern recognition letters 33(2): 191–198.RossG. J.2012“Exponentially weighted moving average charts for detecting concept drift”Pattern recognition letters33(2):191–198Search in Google Scholar
Rouse, M. 2009. Predictive Analytics Definition.RouseM.2009Predictive Analytics DefinitionSearch in Google Scholar
Saurav, S., et al. 2018. “Online anomaly detection with concept drift adaptation using recurrent neural networks.” Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. ACM.SauravS.2018“Online anomaly detection with concept drift adaptation using recurrent neural networks.” Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. ACMSearch in Google Scholar
Sayed, S., Ansari, S. A. and Poonia, R. 2018. “Overview of Concept Drifts Detection Methodology in Data Stream” Handbook of Research on Pattern Engineering System Development for Big Data Analytics. IGI Global, pp. 310–317, doi: 10.4018/978-1-5225-3870-7.ch018.SayedS.AnsariS. A. and PooniaR.2018“Overview of Concept Drifts Detection Methodology in Data Stream” Handbook of Research on Pattern Engineering System Development for Big Data AnalyticsIGI Global, pp.310–317doi:10.4018/978-1-5225-3870-7.ch018Open DOISearch in Google Scholar
Schaik, A. and van. Tapson, J. 2015. Online and Adaptive Pseudoinverse Solutions for ELM Weights. Neurocomputing 149(A): 233–238.SchaikA. and van. TapsonJ.2015Online and Adaptive Pseudoinverse Solutions for ELM WeightsNeurocomputing149(A):233–238Search in Google Scholar
Sidhu, P. and Bhatia, M. P. S. 2018. “A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority”. International Journal of Machine Learning and Cybernetics 9(1): 37–61.SidhuP. and BhatiaM. P. S.2018“A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority”International Journal of Machine Learning and Cybernetics9(1):37–61Search in Google Scholar
Spinosa, E. J., de Carvalho, A. P. de L. F. and Gama, J. 2007. “Olindda: A cluster-based approach for detecting novelty and concept drift in data streams.” Proceedings of the 2007 ACM symposium on Applied computing. ACM.SpinosaE. J.de CarvalhoA. P. de L. F. and GamaJ.2007“Olindda: A cluster-based approach for detecting novelty and concept drift in data streams.” Proceedings of the 2007 ACM symposium on Applied computing. ACMSearch in Google Scholar
Street, W. N. and Kim, Y. 2001. “A streaming ensemble algorithm (SEA) for large-scale classification,” in Proc. 7th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 377–382.StreetW. N. and KimY.2001“A streaming ensemble algorithm (SEA) for large-scale classification,” in Proc. 7th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining,pp.377–382Search in Google Scholar
Tsymbal, A. 2004. The problem of concept drift: definitions and related work. Technical Report TCD-CS-2004-15, The University of Dublin, Trinity College, Department of Computer Science, Dublin, Ireland.TsymbalA.2004The problem of concept drift: definitions and related work. Technical Report TCD-CS-2004-15, The University of Dublin, Trinity College, Department of Computer Science, Dublin, IrelandSearch in Google Scholar
Uddin, V., Rizvi, S. S. H., Hashmani, M. A., Jameel, S. M. and Ansari, T. 2019. September. A Study of Deterioration in Classification Models in Real-Time Big Data Environment. In International Conference of Reliable Information and Communication Technology, Springer, Cham, pp. 79–87.UddinV.RizviS. S. H.HashmaniM. A.JameelS. M. and AnsariT.2019. SeptemberA Study of Deterioration in Classification Models in Real-Time Big Data Environment. In International Conference of Reliable Information and Communication TechnologySpringerCham, pp.79–87Search in Google Scholar
Wadewale, K. and Desai., S. 2015. “Survey on method of drift detection and classification for time varying data set”. International Journal of Research in Engineering and Technology 2(9): 709–713.WadewaleK. and Desai.S.2015“Survey on method of drift detection and classification for time varying data set”International Journal of Research in Engineering and Technology2(9):709–713Search in Google Scholar
Wang, H., Fan, W., Yu, P. S. and Han, J. 2003. “Mining concept-drifting data streams using ensemble classifiers”, In Getoor, L., Senator, T. E., Domingos, P. and Faloutsos, C. (Eds), Association for Computing Machinery, ACM Press, New York, NY, pp. 226–235.WangH.FanW.YuP. S. and HanJ.2003. “Mining concept-drifting data streams using ensemble classifiers”, InGetoorL.SenatorT. E.DomingosP. and FaloutsosC. (Eds)Association for Computing MachineryACM PressNew York, NY, pp.226–235Search in Google Scholar
Webb, G. I., et al. 2016. “Characterizing concept drift”. Data Mining and Knowledge Discovery 30(4): 964–994.WebbG. I.2016“Characterizing concept drift”Data Mining and Knowledge Discovery30(4):964–994Search in Google Scholar
Webb, G. I., et al. 2018. “Analyzing concept drift and shift from sample data”. Data Mining and Knowledge Discovery 32(5): 1179–1199.WebbG. I.2018“Analyzing concept drift and shift from sample data”Data Mining and Knowledge Discovery32(5):1179–1199Search in Google Scholar
Xu, S. and Wang, J. 2016. A Fast-Incremental Extreme Learning Machine Algorithm for Data Streams Classification. Expert Systems with Applications 65: 332–344.XuS. and WangJ.2016A Fast-Incremental Extreme Learning Machine Algorithm for Data Streams ClassificationExpert Systems with Applications65332–344Search in Google Scholar
Xu, S. and Wang, J. 2017. Dynamic Extreme Learning Machine for Stream Classification. Neurocomputing 238(A): 433–449.XuS. and WangJ.2017Dynamic Extreme Learning Machine for Stream ClassificationNeurocomputing238(A):433–449Search in Google Scholar
Yasumura, Y., Kitani, N. and Uehara, K. 2007. “Quick Adaptation to Changing Concepts by Sensitive Detection.” International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems Springer, Berlin and Heidelberg.YasumuraY.KitaniN. and UeharaK.2007“Quick Adaptation to Changing Concepts by Sensitive Detection.” International Conference on Industrial, Engineering and Other Applications of Applied Intelligent SystemsSpringerBerlin and HeidelbergSearch in Google Scholar
Zang, W., Zhang, P., Zhou, C. and Guo, L. 2014. Comparative Study Between Incremental and Ensemble Learning on Data Stream: Case Study. Journal of Big Data 1(1): 1–16.ZangW.ZhangP.ZhouC. and GuoL.2014Comparative Study Between Incremental and Ensemble Learning on Data Stream: Case StudyJournal of Big Data1(1):1–16Search in Google Scholar
Zeira, G., Maimon, O., Last, M. and Rokach, L. 2004. “Data mining in time series databases”, In Last, M., Kandel, A. and Bunke, H. (Eds), Data Mining in Time Series Databases, Volume 57, Chapter Change Detection in Classification Models Induced from Time-Series Data, World Scientific, Singapore, pp. 101–125, Available at: https://www.worldscientific.com/page/about/corporate-profile.ZeiraG.MaimonO.LastM. and RokachL.2004. “Data mining in time series databases”, InLastM.KandelA. and BunkeH. (Eds)Data Mining in Time Series Databases, Volume 57, Chapter Change Detection in Classification Models Induced from Time-Series DataWorld ScientificSingapore, pp.101–125, Available at:https://www.worldscientific.com/page/about/corporate-profileSearch in Google Scholar
Zhai, J., Wang, J. and Wang, X. 2014. “Ensemble Online Sequential Extreme Learning Machine for Large Dataset Classification”, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, pp. 2250–2255, doi: 10.1109/SMC.2014.6974260.ZhaiJ.WangJ. and WangX.2014. “Ensemble Online Sequential Extreme Learning Machine for Large Dataset Classification”,2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC)San Diego, CA, pp.2250–2255, doi:10.1109/SMC.2014.6974260Search in Google Scholar
Zliobaite, I. 2010. Learning under Concept Drift: an Overview. Cornell University Library, pp. 1–36, doi: arxiv.org/abs/1010.4784.ZliobaiteI.2010Learning under Concept Drift: an OverviewCornell University Library, pp.1–36, doi: arxiv.org/abs/1010.4784.Search in Google Scholar
Zliobaite, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A. 2014. A Survey on Concept Drift Adaptation. ACM Computer Survey 46(4): 1–37.ZliobaiteI.BifetA.PechenizkiyM. and BouchachiaA.2014A Survey on Concept Drift AdaptationACM Computer Survey46(4)1–37Search in Google Scholar
Zliobaite, I., et al., 2012. Next Challenges for Adaptive Learning Systems. ACM SIGKDD Explorations Newsletter 14(1): 48.ZliobaiteI.2012Next Challenges for Adaptive Learning SystemsACM SIGKDD Explorations Newsletter14(1):48Search in Google Scholar