Extensions to the CART algorithm
International Journal of Man-Machine Studies
C4.5: programs for machine learning
C4.5: programs for machine learning
Combining the results of several neural network classifiers
Neural Networks
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in the presence of concept drift and hidden contexts
Machine Learning
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Induction of Decision Trees
Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Learning fuzzy classification rules from labeled data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Online ensemble learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Bounds on Error Expectation for Support Vector Machines
Neural Computation
Extensions of vector quantization for incremental clustering
Pattern Recognition
Adaptive mixtures of local experts
Neural Computation
Evolving fuzzy classifiers using different model architectures
Fuzzy Sets and Systems
On Human-Machine Interaction during Online Image Classifier Training
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
Evolving Vector Quantization for Classification of On-Line Data Streams
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
An on-line interactive self-adaptive image classification framework
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Online pattern classification with multiple neural network systems: an experimental study
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multilevel information fusion approach for visual quality inspection
Information Fusion
On-line anomaly detection and resilience in classifier ensembles
Pattern Recognition Letters
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To process the large amounts of data industrial systems are producing nowadays, machine learning techniques have shown their usefulness in many applications. As the amounts of data being generated are getting huge, the need for machine learning methods which can deal with them in an appropriate way - i.e. methods which can be adapted incrementally - becomes very important. Ensembles of classifiers have been shown to be able to improve the predictive accuracy as well as the robustness of single classification methods. In this work novel incremental variants of several well-known classifier fusion methods (Fuzzy Integral, Decision Templates, Dempster-Shafer Combination and Discounted Dempster-Shafer Combination) are presented. Furthermore, a novel incremental classifier fusion method called Incremental Direct Cluster-based ensemble will be introduced, which exploits an evolving clustering approach. A flexible and interactive framework for on-line learning will be introduced, in which the ensemble methods are adapted incrementally in a sample-wise manner together with their base classifiers. The performance of this framework and the proposed incremental classifiers fusion methods therein are evaluated on five real-world visual quality inspection tasks, captured on-line from an industrial CD imprint production process, together with five data sets from the UCI repository.