Dynamic Correlation-Based Feature Selection for Feature Drifts in Data Streams
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Ana L. C. Bazzan | Mariana Recamonde Mendoza | Jorge Cristhian Chamby-Diaz | A. Bazzan | M. R. Mendoza | J. C. Chamby-Diaz
[1] Li Wan,et al. Heterogeneous Ensemble for Feature Drifts in Data Streams , 2012, PAKDD.
[2] Jean Paul Barddal,et al. A survey on feature drift adaptation: Definition, benchmark, challenges and future directions , 2017, J. Syst. Softw..
[3] Wee Keong Ng,et al. A survey on data stream clustering and classification , 2015, Knowledge and Information Systems.
[4] Gong Xiu,et al. An Incremental Bayes Classification Model , 2002 .
[5] Mark A. Hall,et al. Correlation-based Feature Selection for Machine Learning , 2003 .
[6] João Gama,et al. Random rules from data streams , 2013, SAC '13.
[7] Jean Paul Barddal,et al. Iterative subset selection for feature drifting data streams , 2018, SAC.
[8] Robi Polikar,et al. Incremental Learning of Concept Drift in Nonstationary Environments , 2011, IEEE Transactions on Neural Networks.
[9] G. Hommel,et al. Improvements of General Multiple Test Procedures for Redundant Systems of Hypotheses , 1988 .
[10] M. Harries. SPLICE-2 Comparative Evaluation: Electricity Pricing , 1999 .
[11] Marcus A. Maloof,et al. Dynamic weighted majority: a new ensemble method for tracking concept drift , 2003, Third IEEE International Conference on Data Mining.
[12] Albert Bifet,et al. Efficient Online Evaluation of Big Data Stream Classifiers , 2015, KDD.
[13] João Gama,et al. A survey on concept drift adaptation , 2014, ACM Comput. Surv..
[14] D. H. D. West. Updating mean and variance estimates: an improved method , 1979, CACM.
[15] Talel Abdessalem,et al. Adaptive random forests for evolving data stream classification , 2017, Machine Learning.
[16] Jean Paul Barddal,et al. A Survey on Ensemble Learning for Data Stream Classification , 2017, ACM Comput. Surv..
[17] Jean Paul Barddal,et al. Analyzing the Impact of Feature Drifts in Streaming Learning , 2015, ICONIP.
[18] Gerhard Widmer,et al. Learning in the presence of concept drift and hidden contexts , 2004, Machine Learning.
[19] Geoff Hulten,et al. Mining time-changing data streams , 2001, KDD '01.
[20] Jesús S. Aguilar-Ruiz,et al. Knowledge discovery from data streams , 2009, Intell. Data Anal..
[21] Francisco Herrera,et al. A survey on data preprocessing for data stream mining: Current status and future directions , 2017, Neurocomputing.
[22] E. S. Page. CONTINUOUS INSPECTION SCHEMES , 1954 .
[23] Grigorios Tsoumakas,et al. Tracking recurring contexts using ensemble classifiers: an application to email filtering , 2009, Knowledge and Information Systems.
[24] William Nick Street,et al. A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.
[25] Geoff Holmes,et al. MOA: Massive Online Analysis , 2010, J. Mach. Learn. Res..
[26] Ricard Gavaldà,et al. Adaptive Learning from Evolving Data Streams , 2009, IDA.
[27] Lukasz Golab,et al. Issues in data stream management , 2003, SGMD.
[28] Ricard Gavaldà,et al. Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.