Online Ensemble Learning of Data Streams with Gradually Evolved Classes
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[1] Jesús S. Aguilar-Ruiz,et al. Knowledge discovery from data streams , 2009, Intell. Data Anal..
[2] Stuart J. Russell,et al. Online bagging and boosting , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.
[3] Frank Kirchner,et al. Performance evaluation of EANT in the robocup keepaway benchmark , 2007, ICMLA 2007.
[4] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[5] A. Bifet,et al. Early Drift Detection Method , 2005 .
[6] Rui Wang,et al. Towards social user profiling: unified and discriminative influence model for inferring home locations , 2012, KDD.
[7] Vasant Honavar,et al. Learn++: an incremental learning algorithm for supervised neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.
[8] Robi Polikar,et al. Learn$^{++}$ .NC: Combining Ensemble of Classifiers With Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes , 2009, IEEE Transactions on Neural Networks.
[9] Seetha Hari,et al. Learning From Imbalanced Data , 2019, Advances in Computer and Electrical Engineering.
[10] Alexander J. Smola,et al. Online learning with kernels , 2001, IEEE Transactions on Signal Processing.
[11] João Gama,et al. Learning with Drift Detection , 2004, SBIA.
[12] Xin Yao,et al. The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift , 2010, IEEE Transactions on Knowledge and Data Engineering.
[13] Bhavani M. Thuraisingham,et al. Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints , 2011, IEEE Transactions on Knowledge and Data Engineering.
[14] Charu C. Aggarwal,et al. Classification and Adaptive Novel Class Detection of Feature-Evolving Data Streams , 2013, IEEE Transactions on Knowledge and Data Engineering.
[15] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[16] Xin Yao,et al. Resampling-Based Ensemble Methods for Online Class Imbalance Learning , 2015, IEEE Transactions on Knowledge and Data Engineering.
[17] Taeho Jo,et al. Class imbalances versus small disjuncts , 2004, SKDD.
[18] João Gama,et al. Recurrent concepts in data streams classification , 2013, Knowledge and Information Systems.
[19] Shonali Krishnaswamy,et al. Mining data streams: a review , 2005, SGMD.
[20] Russel Pears,et al. Mining Recurrent Concepts in Data Streams Using the Discrete Fourier Transform , 2014, DaWaK.
[21] Xin Yao,et al. Online Class Imbalance Learning and its Applications in Fault Detection , 2013, Int. J. Comput. Intell. Appl..
[22] Charu C. Aggarwal,et al. Stream Classification with Recurring and Novel Class Detection Using Class-Based Ensemble , 2012, 2012 IEEE 12th International Conference on Data Mining.
[23] William Nick Street,et al. A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.
[24] Charu C. Aggarwal,et al. Addressing Concept-Evolution in Concept-Drifting Data Streams , 2010, 2010 IEEE International Conference on Data Mining.
[25] Nathalie Japkowicz,et al. Concept-Learning in the Presence of Between-Class and Within-Class Imbalances , 2001, Canadian Conference on AI.
[26] Geoff Hulten,et al. Mining high-speed data streams , 2000, KDD '00.
[27] Xin Yao,et al. A learning framework for online class imbalance learning , 2013, 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL).
[28] Gregory Ditzler,et al. Incremental Learning of New Classes in Unbalanced Datasets: Learn + + .UDNC , 2010, MCS.
[29] Ricard Gavaldà,et al. Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.
[30] Raj Bhatnagar,et al. Tracking recurrent concept drift in streaming data using ensemble classifiers , 2007, Sixth International Conference on Machine Learning and Applications (ICMLA 2007).
[31] Gerhard Widmer,et al. Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.
[32] João Gama,et al. A survey on concept drift adaptation , 2014, ACM Comput. Surv..
[33] Geoff Holmes,et al. New ensemble methods for evolving data streams , 2009, KDD.
[34] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[35] Rong Jin,et al. Non-parametric Mixture Models for Clustering , 2010, SSPR/SPR.
[36] Charu C. Aggarwal,et al. Detecting Recurring and Novel Classes in Concept-Drifting Data Streams , 2011, 2011 IEEE 11th International Conference on Data Mining.
[37] Zhi-Hua Zhou,et al. Hybrid decision tree , 2002, Knowl. Based Syst..
[38] Gregory Ditzler,et al. Incremental learning of new classes from unbalanced data , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[39] Anil K. Jain,et al. Algorithms for Clustering Data , 1988 .
[40] Jerzy Stefanowski,et al. Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[41] Philip S. Yu,et al. Classifying Data Streams with Skewed Class Distributions and Concept Drifts , 2008, IEEE Internet Computing.
[42] Jing Liu,et al. Ambiguous decision trees for mining concept-drifting data streams , 2009, Pattern Recognit. Lett..
[43] Xin Yao,et al. DDD: A New Ensemble Approach for Dealing with Concept Drift , 2012, IEEE Transactions on Knowledge and Data Engineering.
[44] Bhavani M. Thuraisingham,et al. Integrating Novel Class Detection with Classification for Concept-Drifting Data Streams , 2009, ECML/PKDD.
[45] Robi Polikar,et al. Learning concept drift in nonstationary environments using an ensemble of classifiers based approach , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).