System identification: theory for the user
System identification: theory for the user
The nature of statistical learning theory
The nature of statistical learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
Fuzzy Modeling for Control
NEFCLASS-X — a Soft Computing Tool to Build Readable Fuzzy Classifiers
BT Technology Journal
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Gradient based variable forgetting factor RLS algorithm
Signal Processing
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Extensions of vector quantization for incremental clustering
Pattern Recognition
Tracking Recurrent Concept Drift in Streaming Data Using Ensemble Classifiers
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Efficient instance-based learning on data streams
Intelligent Data Analysis
Evolving fuzzy classifiers using different model architectures
Fuzzy Sets and Systems
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
An on-line interactive self-adaptive image classification framework
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Adaptive inferential sensors based on evolving fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
Time stamping in the presence of latency and drift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Artificial recurrence for classification of streaming data with concept shift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
On employing fuzzy modeling algorithms for the valuation of residential premises
Information Sciences: an International Journal
A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system
Applied Soft Computing
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Adaptive fuzzy control of aircraft wing-rock motion
Applied Soft Computing
A fast learning algorithm for evolving neo-fuzzy neuron
Applied Soft Computing
Evolving intelligent algorithms for the modelling of brain and eye signals
Applied Soft Computing
Evolving intelligent system for the modelling of nonlinear systems with dead-zone input
Applied Soft Computing
Water leakage forecasting: the application of a modified fuzzy evolving algorithm
Applied Soft Computing
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In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact that their structure (rule base and parameters) is not fixed and not pre-determined, but is extracted from data streams on-line and in an incremental manner. When dealing with so-called drifts and s hifts in data streams, one needs to take into account (1) automatic detection of drifts and shifts and (2) automatic reaction to the drifts and shifts. This is important to avoid interruptions in the learning process and downtrends in predictive accuracy. To address the first problem, we propose an approach based on the concept fuzzy rule age. The second problem is addressed by including gradual forgetting of (1) antecedent parts and (2) consequent parameters. The latter can be achieved by including a forgetting factor in the recursive local learning process of the parameters, whose value is automatically extracted based on the intensity of the shift/drift. For addressing the former problem, we introduce two alternative methods: one is based on the evolving density-based clustering (eClustering) used to form the antecedents in the eTS approach; the other is based on the automatic adaptation of the learning rate of the evolving vector quantization (eVQ) method used to form the antecedent in the FLEXFIS approach. The paper concludes with an empirical evaluation of the impact of the proposed approaches in (on-line) real-world data sets in which drifts and shifts occur.