A Nearest Hyperrectangle Learning Method
Machine Learning
A resource-allocating network for function interpolation
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
Democracy in neural nets: voting schemes for classification
Neural Networks
A function estimation approach to sequential learning with neural networks
Neural Computation
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Methods for combining experts' probability assessments
Neural Computation
Optimal combinations of pattern classifiers
Pattern Recognition Letters
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning from Noisy Data
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Online ensemble learning
Online Ensemble Learning: An Empirical Study
Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Incremental Learning By Decomposition
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Adaptive mixtures of local experts
Neural Computation
Adaptive Mechanisms for Classification Problems with Drifting Data
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Dynamic Coverage Based on Neural Gas Learning Algorithm for Wireless Sensor Network
International Journal of Distributed Sensor Networks
Using Growing Neural Gas Networks to Represent Visual Object Knowledge
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Evolutionary Fuzzy ARTMAP Neural Networks and their Applications to Fault Detection and Diagnosis
Neural Processing Letters
Fuzzy classification in dynamic environments
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Recent progress in natural computation and knowledge discovery
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An Ensemble-Based Incremental Learning Approach to Data Fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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
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Self-adaptation is an inherent part of any natural and intelligent system. Specifically, it is about the ability of a system to reconcile its requirements or goal of existence with the environment it is interacting with, by adopting an optimal behavior. Self-adaptation becomes crucial when the environment changes dynamically over time. In this paper, we investigate self-adaptation of classification systems at three levels: (1) natural adaptation of the base learners to change in the environment, (2) contributive adaptation when combining the base learners in an ensemble, and (3) structural adaptation of the combination as a form of dynamic ensemble. The present study focuses on neural network classification systems to handle a special facet of self-adaptation, that is, incremental learning (IL). With IL, the system self-adjusts to accommodate new and possibly non-stationary data samples arriving over time. The paper discusses various IL algorithms and shows how the three adaptation levels are inherent in the system's architecture proposed and how this architecture is efficient in dealing with dynamic change in the presence of various types of data drift when applying these IL algorithms.