KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift
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[1] Ludmila I. Kuncheva,et al. Adaptive Learning Rate for Online Linear Discriminant Classifiers , 2008, SSPR/SPR.
[2] Ricard Gavaldà,et al. Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.
[3] Gregory Ditzler,et al. Learning in Nonstationary Environments: A Survey , 2015, IEEE Computational Intelligence Magazine.
[4] Harry Zhang,et al. The Optimality of Naive Bayes , 2004, FLAIRS.
[5] M. Anusha,et al. Big Data-Survey , 2016 .
[6] Grigorios Tsoumakas,et al. Dealing with Concept Drift and Class Imbalance in Multi-Label Stream Classification , 2011, IJCAI.
[7] Analía Amandi,et al. eTeacher: Providing personalized assistance to e-learning students , 2008, Comput. Educ..
[8] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.
[9] Stuart J. Russell,et al. Online bagging and boosting , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.
[10] Richard C. Atkinson,et al. Human Memory: A Proposed System and its Control Processes , 1968, Psychology of Learning and Motivation.
[11] Sahibsingh A. Dudani. The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.
[12] S. Venkatasubramanian,et al. An Information-Theoretic Approach to Detecting Changes in Multi-Dimensional Data Streams , 2006 .
[13] William Nick Street,et al. A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.
[14] Geoff Holmes,et al. MOA: Massive Online Analysis , 2010, J. Mach. Learn. Res..
[15] Indre Zliobaite,et al. How good is the Electricity benchmark for evaluating concept drift adaptation , 2013, ArXiv.
[16] A. Bifet,et al. Early Drift Detection Method , 2005 .
[17] Marcus A. Maloof,et al. Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts , 2007, J. Mach. Learn. Res..
[18] Geoff Holmes,et al. Efficient data stream classification via probabilistic adaptive windows , 2013, SAC '13.
[19] G. A. Miller. THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .
[20] Gerhard Widmer,et al. Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.
[21] João Gama,et al. Learning with Drift Detection , 2004, SBIA.
[22] João Gama,et al. A survey on concept drift adaptation , 2014, ACM Comput. Surv..
[23] Ck Cheng,et al. The Age of Big Data , 2015 .
[24] Carlo Zaniolo,et al. An Adaptive Nearest Neighbor Classification Algorithm for Data Streams , 2005, PKDD.
[25] Y. Dudai. The neurobiology of consolidations, or, how stable is the engram? , 2004, Annual review of psychology.
[26] Geoff Holmes,et al. Leveraging Bagging for Evolving Data Streams , 2010, ECML/PKDD.
[27] Thorsten Joachims,et al. Detecting Concept Drift with Support Vector Machines , 2000, ICML.
[28] Li Guo,et al. Enabling Fast Lazy Learning for Data Streams , 2011, 2011 IEEE 11th International Conference on Data Mining.
[29] João Gama,et al. Accurate decision trees for mining high-speed data streams , 2003, KDD '03.
[30] Robi Polikar,et al. Incremental Learning of Concept Drift in Nonstationary Environments , 2011, IEEE Transactions on Neural Networks.
[31] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[32] Antoine Cornuéjols,et al. Online Learning: Searching for the Best Forgetting Strategy under Concept Drift , 2013, ICONIP.
[33] Yoav Freund,et al. A Short Introduction to Boosting , 1999 .
[34] M. Harries. SPLICE-2 Comparative Evaluation: Electricity Pricing , 1999 .
[35] Geoff Hulten,et al. Mining high-speed data streams , 2000, KDD '00.
[36] Lida Xu,et al. The internet of things: a survey , 2014, Information Systems Frontiers.
[37] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[38] Stuart J. Russell,et al. Experimental comparisons of online and batch versions of bagging and boosting , 2001, KDD '01.