Recent trends in streaming data analysis, concept drift and analysis of dynamic data sets

Today, many data are not any longer static but occur as dynamic data streams with high velocity, variability and volume. This leads to new challenges to be addressed by novel or adapted algorithms. In this tutorial we provide an introduction into the field of streaming data analysis summarizing its major characteristics and highlighting important research directions in the analysis of dynamic data.

[1]  Martial Mermillod,et al.  The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects , 2013, Front. Psychol..

[2]  Ioannis Mitliagkas,et al.  Memory Limited, Streaming PCA , 2013, NIPS.

[3]  Ilja Kuzborskij,et al.  Nonparametric Online Regression while Learning the Metric , 2017, NIPS.

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

[5]  P. R. Deshmukh,et al.  METHODS FOR INCREMENTAL LEARNING: A SURVEY , 2013 .

[6]  Abdelhamid Bouchachia,et al.  Online Bayesian shrinkage regression , 2020, Neural Computing and Applications.

[7]  Mahardhika Pratama,et al.  A novel online real-time classifier for multi-label data streams , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[8]  Feng Huang,et al.  Robust Prototype-Based Learning on Data Streams , 2018, IEEE Transactions on Knowledge and Data Engineering.

[9]  Gregory Ditzler,et al.  Learning in Nonstationary Environments: A Survey , 2015, IEEE Computational Intelligence Magazine.

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

[11]  Shonali Krishnaswamy,et al.  Mining data streams: a review , 2005, SGMD.

[12]  Gail A. Carpenter,et al.  ARTMAP: a self-organizing neural network architecture for fast supervised learning and pattern recognition , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[13]  Qi Liu,et al.  Incremental semi-supervised learning on streaming data , 2019, Pattern Recognit..

[14]  Suresh Venkatasubramanian,et al.  Incremental Multi-Dimensional Scaling , 2010 .

[15]  Gert Cauwenberghs,et al.  SVM incremental learning, adaptation and optimization , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

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

[17]  Michael Biehl,et al.  Statistical Mechanics of On-Line Learning Under Concept Drift , 2018, Entropy.

[18]  Theodoros Damoulas,et al.  Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences , 2018, NeurIPS.

[19]  Johannes Fürnkranz,et al.  Beta Distribution Drift Detection for Adaptive Classifiers , 2018, ESANN.

[20]  Parag Kulkarni,et al.  Incremental Learning: Areas and Methods - A Survey , 2012 .

[21]  Heiko Wersing,et al.  Incremental on-line learning: A review and comparison of state of the art algorithms , 2018, Neurocomputing.

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

[23]  Ludmila I. Kuncheva,et al.  A taxonomic look at instance-based stream classifiers , 2018, Neurocomputing.

[24]  Talel Abdessalem,et al.  Adaptive random forests for evolving data stream classification , 2017, Machine Learning.

[25]  Bhavani M. Thuraisingham,et al.  Unsupervised deep embedding for novel class detection over data stream , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[26]  Volkan Cevher,et al.  Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data , 2017, NIPS.

[27]  Sikun Li,et al.  An incremental extremely random forest classifier for online learning and tracking , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[28]  Cesare Alippi,et al.  Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes , 2008, IEEE Transactions on Neural Networks.

[29]  Geoff Hulten,et al.  A General Framework for Mining Massive Data Streams , 2003 .

[30]  João Paulo Papa,et al.  An Overview on Concept Drift Learning , 2019, IEEE Access.

[31]  Frank-Michael Schleif,et al.  Reactive Soft Prototype Computing for frequent reoccurring Concept Drift , 2019, ESANN.

[32]  E. Oja.  Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.

[33]  Heiko Wersing,et al.  Tackling heterogeneous concept drift with the Self-Adjusting Memory (SAM) , 2017, Knowledge and Information Systems.

[34]  Xin Yao,et al.  A Systematic Study of Online Class Imbalance Learning With Concept Drift , 2018 .

[35]  Geoff Hulten,et al.  Mining high-speed data streams , 2000, KDD '00.

[36]  Albert Bifet,et al.  Efficient Online Evaluation of Big Data Stream Classifiers , 2015, KDD.

[37]  Florent Masseglia,et al.  Parameterless outlier detection in data streams , 2009, SAC '09.

[38]  Lorenzo Livi,et al.  Concept Drift and Anomaly Detection in Graph Streams , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[39]  Thibault Gisselbrecht,et al.  Bandit algorithms for real-time data capture on large social medias , 2018, ArXiv.

[40]  Francisco Herrera,et al.  A survey on data preprocessing for data stream mining: Current status and future directions , 2017, Neurocomputing.

[41]  Sylvain Lamprier,et al.  Dynamic Data Capture from Social Media Streams: A Contextual Bandit Approach , 2016, ICWSM.

[42]  Lijun Zhang,et al.  Adaptive Online Learning in Dynamic Environments , 2018, NeurIPS.

[43]  Barbara Hammer,et al.  Incremental learning algorithms and applications , 2016, ESANN.

[44]  Yi Yang,et al.  A Framework of Online Learning with Imbalanced Streaming Data , 2017, AAAI.

[45]  Juha Röning,et al.  Importance of user inputs while using incremental learning to personalize human activity recognition models , 2019, ESANN.

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

[47]  Kai Ming Ting,et al.  Fast Anomaly Detection for Streaming Data , 2011, IJCAI.

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

[49]  Heiko Wersing,et al.  Self-Adjusting Memory: How to Deal with Diverse Drift Types , 2017, IJCAI.

[50]  Heiko Wersing,et al.  KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

[51]  Indre Zliobaite,et al.  Learning under Concept Drift: an Overview , 2010, ArXiv.

[52]  Robi Polikar,et al.  Incremental Learning of Concept Drift in Nonstationary Environments , 2011, IEEE Transactions on Neural Networks.

[53]  Roberto Souto Maior de Barros,et al.  A large-scale comparison of concept drift detectors , 2018, Inf. Sci..