Creating User Profiles from a Command-Line Interface: A Statistical Approach
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Evolving granular classification neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An evolving fuzzy neural network based on the mapping of similarities
IEEE Transactions on Fuzzy Systems
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
eFSM: a novel online neural-fuzzy semantic memory model
IEEE Transactions on Neural Networks
A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning
IEEE Transactions on Neural Networks
Hierarchical fuzzy clustering decision tree for classifying recipes of ion implanter
Expert Systems with Applications: An International Journal
Human Activity Recognition in Intelligent Home Environments: An Evolving Approach
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases
Artificial Intelligence in Medicine
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
Granular approach for evolving system modeling
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
RFCMAC: A novel reduced localized neuro-fuzzy system approach to knowledge extraction
Expert Systems with Applications: An International Journal
A GMDH-based fuzzy modeling approach for constructing TS model
Fuzzy Sets and Systems
Digital Signal Processing
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
Density-based averaging - A new operator for data fusion
Information Sciences: an International Journal
Online activity recognition using evolving classifiers
Expert Systems with Applications: An International Journal
Evolving granular neural networks from fuzzy data streams
Neural Networks
Conjecturable knowledge discovery: A fuzzy clustering approach
Fuzzy Sets and Systems
An adaptive ensemble classifier for mining concept drifting data streams
Expert Systems with Applications: An International Journal
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
International Journal of Organizational and Collective Intelligence
A fast learning algorithm for evolving neo-fuzzy neuron
Applied Soft Computing
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A new approach to the online classification of streaming data is introduced in this paper. It is based on a self-developing (evolving) fuzzy-rule-based (FRB) classifier system of Takagi-Sugeno ( eTS) type. The proposed approach, called eClass (evolving class ifier), includes different architectures and online learning methods. The family of alternative architectures includes: 1) eClass0, with the classifier consequents representing class label and 2) the newly proposed method for regression over the features using a first-order eTS fuzzy classifier, eClass1. An important property of eClass is that it can start learning ldquofrom scratch.rdquo Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for eClass (the number may grow, with new class labels being added by the online learning process). In the event that an initial FRB exists, eClass can evolve/develop it further based on the newly arrived data. The proposed approach addresses the practical problems of the classification of streaming data (video, speech, sensory data generated from robotic, advanced industrial applications, financial and retail chain transactions, intruder detection, etc.). It has been successfully tested on a number of benchmark problems as well as on data from an intrusion detection data stream to produce a comparison with the established approaches. The results demonstrate that a flexible (with evolving structure) FRB classifier can be generated online from streaming data achieving high classification rates and using limited computational resources.