相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。An extensive study of C-SMOTE, a Continuous Synthetic Minority Oversampling Technique for Evolving Data Streams
Alessio Bernardo et al.
EXPERT SYSTEMS WITH APPLICATIONS (2022)
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams
Alberto Cano et al.
MACHINE LEARNING (2022)
Adaptive ensemble of self-adjusting nearest neighbor subspaces for multi-label drifting data streams
Gavin Alberghini et al.
NEUROCOMPUTING (2022)
A Diversity Framework for Dealing With Multiple Types of Concept Drift Based on Clustering in the Model Space
Chun Wai Chiu et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)
Meta-ADD: A meta-learning based pre-trained model for concept drift active detection
Hang Yu et al.
INFORMATION SCIENCES (2022)
Learning Under Concept Drift for Regression-A Systematic Literature Review
Marilia Lima et al.
IEEE ACCESS (2022)
Data stream clustering: a review
Alaettin Zubaroglu et al.
ARTIFICIAL INTELLIGENCE REVIEW (2021)
Using spectral entropy and bernoulli map to handle concept drift
Rohgi Toshio Meneses Chikushi et al.
EXPERT SYSTEMS WITH APPLICATIONS (2021)
Improving the performance of bagging ensembles for data streams through mini-batching
Guilherme Cassales et al.
INFORMATION SCIENCES (2021)
Adaptive online incremental learning for evolving data streams
Si -si Zhang et al.
APPLIED SOFT COMPUTING (2021)
Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams
Pawel Zyblewski et al.
INFORMATION FUSION (2021)
Semi-supervised classification on data streams with recurring concept drift and concept evolution
Xiulin Zheng et al.
KNOWLEDGE-BASED SYSTEMS (2021)
Self-adjusting k nearest neighbors for continual learning from multi-label drifting data streams
Martha Roseberry et al.
NEUROCOMPUTING (2021)
Hyperparameter self-tuning for data streams
Bruno Veloso et al.
INFORMATION FUSION (2021)
Online ensemble learning algorithm for imbalanced data stream
Du Hongle et al.
APPLIED SOFT COMPUTING (2021)
Automated adaptation strategies for stream learning
Rashid Bakirov et al.
MACHINE LEARNING (2021)
Kappa Updated Ensemble for drifting data stream mining
Alberto Cano et al.
MACHINE LEARNING (2020)
Handling concept drift via model reuse
Peng Zhao et al.
MACHINE LEARNING (2020)
Exploiting evolving micro-clusters for data stream classification with emerging class detection
Salah Ud Din et al.
INFORMATION SCIENCES (2020)
Analyzing concept drift: A case study in the financial sector
Andres R. Masegosa et al.
INTELLIGENT DATA ANALYSIS (2020)
Generalizing from a Few Examples: A Survey on Few-shot Learning
Yaqing Wang et al.
ACM COMPUTING SURVEYS (2020)
Evaluating time series forecasting models: an empirical study on performance estimation methods
Vitor Cerqueira et al.
MACHINE LEARNING (2020)
Towards explainable deep neural networks (xDNN)
Plamen Angelov et al.
NEURAL NETWORKS (2020)
C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams
Alessio Bernardo et al.
2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) (2020)
Learning in the presence of concept recurrence in data stream clustering
K. Namitha et al.
JOURNAL OF BIG DATA (2020)
On learning guarantees to unsupervised concept drift detection on data streams
Rodrigo F. de Mello et al.
EXPERT SYSTEMS WITH APPLICATIONS (2019)
Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams
Alberto Cano et al.
PATTERN RECOGNITION (2019)
Multi-Label Punitive kNN with Self-Adjusting Memory for Drifting Data Streams
Martha Roseberry et al.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2019)
Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms
Matthias Carnein et al.
BUSINESS & INFORMATION SYSTEMS ENGINEERING (2019)
Incremental Market Behavior Classification in Presence of Recurring Concepts
Andres L. Suarez-Cetrulo et al.
ENTROPY (2019)
Recurring concept meta-learning for evolving data streams
Robert Anderson et al.
EXPERT SYSTEMS WITH APPLICATIONS (2019)
An overview and comprehensive comparison of ensembles for concept drift
Roberto Souto Maior de Barros et al.
INFORMATION FUSION (2019)
Enhancing stock market prediction with extended coupled hidden Markov model over multi-sourced data
Xi Zhang et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2019)
Data stream mining: methods and challenges for handling concept drift
Scott Wares et al.
SN APPLIED SCIENCES (2019)
A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority
Parneeta Sidhu et al.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2018)
Modeling recurring concepts in data streams: a graph-based framework
Zahra Ahmadi et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2018)
Online ensemble learning with abstaining classifiers for drifting and noisy data streams
Bartosz Krawczyk et al.
APPLIED SOFT COMPUTING (2018)
Adapting dynamic classifier selection for concept drift
Paulo R. L. Almeida et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
A large-scale comparison of concept drift detectors
Roberto Souto Maior Barros et al.
INFORMATION SCIENCES (2018)
Concept Drift Adaptation by Exploiting Historical Knowledge
Yu Sun et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)
Statistical Mechanics of On-Line Learning Under Concept Drift
Michiel Straat et al.
ENTROPY (2018)
RDDM: Reactive drift detection method
Roberto S. M. Barros et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
On the reliable detection of concept drift from streaming unlabeled data
Tegjyot Singh Sethi et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Multidimensional surrogate stability to detect data stream concept drift
Fausto G. da Costa et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Ensemble learning for data stream analysis: A survey
Bartosz Krawczyk et al.
INFORMATION FUSION (2017)
Data driven modelling based on Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network
Mahardhika Pratama et al.
NEUROCOMPUTING (2017)
A survey on data preprocessing for data stream mining: Current status and future directions
Sergio Ramirez-Gallego et al.
NEUROCOMPUTING (2017)
A Survey on Ensemble Learning for Data Stream Classification
Heitor Murilo Gomes et al.
ACM COMPUTING SURVEYS (2017)
Capturing recurring concepts using discrete Fourier transform
Sripirakas Sakthithasan et al.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE (2016)
Characterizing concept drift
Geoffrey I. Webb et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2016)
Using dynamical systems tools to detect concept drift in data streams
F. G. da Costa et al.
EXPERT SYSTEMS WITH APPLICATIONS (2016)
Model-Based Clustering
Paul D. McNicholas
JOURNAL OF CLASSIFICATION (2016)
Clustering financial time series: New insights from an extended hidden Markov model
Jose G. Dias et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH (2015)
Predicting stock market index using fusion of machine learning techniques
Jigar Patel et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques
Jigar Patel et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Clustering by growing incremental self-organizing neural network
Hao Liu et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Evaluating multiple classifiers for stock price direction prediction
Michel Ballings et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Learning in Nonstationary Environments: A Survey
Gregory Ditzler et al.
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2015)
pClass: An Effective Classifier for Streaming Examples
Mahardhika Pratama et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS (2015)
Online and Non-Parametric Drift Detection Methods Based on Hoeffding's Bounds
Isvani Frias-Blanco et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2015)
Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study
Ammar Shaker et al.
NEUROCOMPUTING (2015)
Towards cost-sensitive adaptation: When is it worth updating your predictive model?
Indre Zliobaite et al.
NEUROCOMPUTING (2015)
Evaluation methods and decision theory for classification of streaming data with temporal dependence
Indre Zliobaite et al.
MACHINE LEARNING (2015)
A Survey on Concept Drift Adaptation
Joao Gama et al.
ACM COMPUTING SURVEYS (2014)
Proposal of a new stability concept to detect changes in unsupervised data streams
Rosane M. M. Vallim et al.
EXPERT SYSTEMS WITH APPLICATIONS (2014)
A comparative study on concept drift detectors
Paulo M. Goncalves et al.
EXPERT SYSTEMS WITH APPLICATIONS (2014)
SURVIVAL ANALYSIS ON DATA STREAMS: ANALYZING TEMPORAL EVENTS IN DYNAMICALLY CHANGING ENVIRONMENTS
Ammar Shaker et al.
INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE (2014)
Mining Recurring Concepts in a Dynamic Feature Space
Joao Bartolo Gomes et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2014)
Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm
Dariusz Brzezinski et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2014)
An online incremental learning support vector machine for large-scale data
Jun Zheng et al.
NEURAL COMPUTING & APPLICATIONS (2013)
Opposite Maps: Vector Quantization Algorithms for Building Reduced-Set SVM and LSSVM Classifiers
Ajalmar R. R. Neto et al.
NEURAL PROCESSING LETTERS (2013)
RCD: A recurring concept drift framework
Paulo Mauricio Goncalves et al.
PATTERN RECOGNITION LETTERS (2013)
Just-In-Time Classifiers for Recurrent Concepts
Cesare Alippi et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2013)
An incremental learning vector quantization algorithm for pattern classification
Ye Xu et al.
NEURAL COMPUTING & APPLICATIONS (2012)
Meta-cognitive Neural Network for classification problems in a sequential learning framework
G. Sateesh Babu et al.
NEUROCOMPUTING (2012)
Learning from concept drifting data streams with unlabeled data
Xindong Wu et al.
NEUROCOMPUTING (2012)
Regime Shifts: Implications for Dynamic Strategies
Mark Kritzman et al.
FINANCIAL ANALYSTS JOURNAL (2012)
Incremental Learning of Concept Drift in Nonstationary Environments
Ryan Elwell et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS (2011)
Ensemble-based classifiers
Lior Rokach
ARTIFICIAL INTELLIGENCE REVIEW (2010)
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
Leandro L. Minku et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)
A Survey on Transfer Learning
Sinno Jialin Pan et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2010)
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Ioannis Katakis et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2010)
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
Plamen P. Angelov et al.
IEEE TRANSACTIONS ON FUZZY SYSTEMS (2008)
Mining in anticipation for concept change: Proactive-reactive prediction in data streams
Ying Yang et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2006)
Prototype selection for dissimilarity-based classifiers
E Pekalska et al.
PATTERN RECOGNITION (2006)
An approach to Online identification of Takagi-Suigeno fuzzy models
PP Angelov et al.
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS (2004)
Dynamic topology representing networks
J Si et al.
NEURAL NETWORKS (2000)
分享论文
