System identification: theory for the user
System identification: theory for the user
Learning in the presence of concept drift and hidden contexts
Machine Learning
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Generation and improvement of fuzzy classifiers with incremental learning using fuzzy RuleNet
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
Adaptive Control
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
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
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Extensions of vector quantization for incremental clustering
Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
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
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving the interpretability of TSK fuzzy models by combining global learning and local learning
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
From approximative to descriptive fuzzy classifiers
IEEE Transactions on Fuzzy Systems
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Towards incremental classifier fusion
Intelligent Data Analysis
SparseFIS: data-driven learning of fuzzy systems with sparsity constraints
IEEE Transactions on Fuzzy Systems
On dynamic soft dimension reduction in evolving fuzzy classifiers
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
On-line valuation of residential premises with evolving fuzzy models
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Time stamping in the presence of latency and drift
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
International Journal of Approximate Reasoning
On employing fuzzy modeling algorithms for the valuation of residential premises
Information Sciences: an International Journal
Evolving fuzzy classifier based on the modified ECM algorithm for pattern classification
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Information Sciences: an International Journal
Evolving fuzzy pattern trees for binary classification on data streams
Information Sciences: an International Journal
Navigating interpretability issues in evolving fuzzy systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Granular Computing and Human-Centricity in Computational Intelligence
International Journal of Software Science and Computational Intelligence
Conjecturable knowledge discovery: A fuzzy clustering approach
Fuzzy Sets and Systems
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
Information Sciences: an International Journal
Fuzzy classifier based on fuzzy support vector machine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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In this paper we present two novel approaches for on-line evolving fuzzy classifiers, called eClass and FLEXFIS-Class. Both methods can be applied with different model architectures, including single model (SM) with class labels as consequents, classification hyper-planes as consequents, and multi-model (MM) architecture. Additionally, eClass can have a multi-input-multi-output (MIMO) architecture with multiple hyper-planes as consequents of each fuzzy rule. The difference between MM and MIMO architectures is that the former one applies one separate and independent fuzzy rule-based (FRB) classifier for each class and is using an indicator labelling scheme, while the latter one applies a single FRB where the rules are MIMO rather than MISO. Both, eClass and FLEXFIS-Class methods are designed to work on a per-sample basis and are thus one-pass, incremental. Additionally, their structure (FRB) is evolving rather than fixed. It adapts their parameters in antecedent and consequent parts with any newly loaded sample. A special emphasis is placed on advanced issues for improving accuracy and robustness, including a thorough comparison between global and local learning of consequent functions, a novel approach for detecting of and reacting on drifts in the data streams and an enhanced outlier treatment strategy. The methods and their extensions according to the advanced issues are evaluated on one benchmark problem of handwritten images recognition as well as on a real-life problem of image classification framework, where images should be classified into good and bad ones during an on-line and interactive production process.