Learning in the Presence of Skew and Missing Labels Through Online Ensembles and Meta-reinforcement Learning

[1]  Xin Yao,et al.  A Systematic Study of Online Class Imbalance Learning With Concept Drift , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[2]  Xin Yao,et al.  Resampling-Based Ensemble Methods for Online Class Imbalance Learning , 2015, IEEE Transactions on Knowledge and Data Engineering.

[3]  Yang Wang,et al.  Cost-sensitive boosting for classification of imbalanced data , 2007, Pattern Recognit..

[4]  Vasant Honavar,et al.  Learn++: an incremental learning algorithm for supervised neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.

[5]  Peter Tiño,et al.  Concept drift detection for online class imbalance learning , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[6]  João Gama,et al.  A survey on concept drift adaptation , 2014, ACM Comput. Surv..

[7]  Zhi-Hua Zhou,et al.  Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.

[8]  Tao Li,et al.  Improving semi-supervised co-forest algorithm in evolving data streams , 2018, Applied Intelligence.

[9]  Francisco Herrera,et al.  Learning from Imbalanced Data Sets , 2018, Springer International Publishing.

[10]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[11]  Sudipto Guha,et al.  Semi-Supervised Learning on Data Streams via Temporal Label Propagation , 2018, ICML.

[12]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[13]  Zhiping Lin,et al.  Voting based weighted online sequential extreme learning machine for imbalance multi-class classification , 2015, 2015 IEEE International Symposium on Circuits and Systems (ISCAS).

[14]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[15]  Sikha Bagui,et al.  Resampling imbalanced data for network intrusion detection datasets , 2021, J. Big Data.

[16]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[17]  Luís Torgo,et al.  A Survey of Predictive Modeling on Imbalanced Domains , 2016, ACM Comput. Surv..

[18]  Marcus A. Maloof,et al.  Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts , 2007, J. Mach. Learn. Res..

[19]  Jaesub Yun,et al.  Automatic Determination of Neighborhood Size in SMOTE , 2016, IMCOM.

[20]  Shankar Vembu,et al.  Chemical gas sensor drift compensation using classifier ensembles , 2012 .

[21]  Hamid Beigy,et al.  An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams , 2016, Knowledge and Information Systems.

[22]  Bartosz Krawczyk,et al.  Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.

[23]  Robi Polikar,et al.  COMPOSE: A Semisupervised Learning Framework for Initially Labeled Nonstationary Streaming Data , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Waqar Ali,et al.  Online reliable semi-supervised learning on evolving data streams , 2020, Inf. Sci..

[25]  Thomas T. Tran,et al.  A hybrid trust model using reinforcement learning and fuzzy logic , 2018, Comput. Intell..

[26]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[27]  Burr Settles,et al.  Active Learning , 2012, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[28]  Herna L. Viktor,et al.  Fast Hoeffding Drift Detection Method for Evolving Data Streams , 2016, ECML/PKDD.

[29]  Didier Stricker,et al.  Introducing a New Benchmarked Dataset for Activity Monitoring , 2012, 2012 16th International Symposium on Wearable Computers.

[30]  Jerzy Stefanowski,et al.  Prequential AUC: properties of the area under the ROC curve for data streams with concept drift , 2017, Knowledge and Information Systems.

[31]  Geoff Hulten,et al.  Mining time-changing data streams , 2001, KDD '01.

[32]  Yongdong Zhang,et al.  Adaptive weighted imbalance learning with application to abnormal activity recognition , 2016, Neurocomputing.

[33]  Talel Abdessalem,et al.  Scikit-Multiflow: A Multi-output Streaming Framework , 2018, J. Mach. Learn. Res..

[34]  João Gama,et al.  Learning with Drift Detection , 2004, SBIA.

[35]  Herna L. Viktor,et al.  SCUT: Multi-class imbalanced data classification using SMOTE and cluster-based undersampling , 2015, 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K).

[36]  W. Michalowski,et al.  Multi-class imbalanced semi-supervised learning from streams through online ensembles , 2020, 2020 International Conference on Data Mining Workshops (ICDMW).

[37]  Yijing Li,et al.  Learning from class-imbalanced data: Review of methods and applications , 2017, Expert Syst. Appl..

[38]  William Nick Street,et al.  A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.

[39]  Xin Yao,et al.  A learning framework for online class imbalance learning , 2013, 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL).

[40]  Alexey Tsymbal,et al.  The problem of concept drift: definitions and related work , 2004 .

[41]  Guangquan Zhang,et al.  Learning under Concept Drift: A Review , 2019, IEEE Transactions on Knowledge and Data Engineering.

[42]  Sergey Levine,et al.  Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.

[43]  Chen Huang,et al.  Reliable Semi-supervised Learning , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[44]  Xiaojin Zhu,et al.  Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[45]  Gregory Ditzler,et al.  Incremental Learning of Concept Drift from Streaming Imbalanced Data , 2013, IEEE Transactions on Knowledge and Data Engineering.

[46]  Stuart J. Russell,et al.  Online bagging and boosting , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[47]  Ricard Gavaldà,et al.  Learning from Time-Changing Data with Adaptive Windowing , 2007, SDM.

[48]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[49]  K. Nageswara Rao,et al.  Undersampled $$K$$K-means approach for handling imbalanced distributed data , 2014, Progress in Artificial Intelligence.

[50]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..

[51]  Haym Hirsh,et al.  A Quantitative Study of Small Disjuncts , 2000, AAAI/IAAI.