A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
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Fuzzy Sets and Systems - Possibility theory and fuzzy logic
Pattern Classification (2nd Edition)
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The Journal of Machine Learning Research
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Pattern Recognition
Evolving fuzzy classifiers using different model architectures
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International Journal of Approximate Reasoning
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Image and Vision Computing
An on-line interactive self-adaptive image classification framework
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolving Fuzzy-Rule-Based Classifiers From Data Streams
IEEE Transactions on Fuzzy Systems
Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Information Sciences: an International Journal
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This paper deals with the problem of dynamic dimension reduction during the on-line update and evolution of fuzzy classifiers. With 'dynamic' it is meant that the importance of features for discriminating between the classes changes over time when new data is sent into the classifiers' update mechanisms. In order to avoid discontinuity in the incremental learning process, i.e. permanently exchanging some features in the input structure of the fuzzy classifiers, we include feature weights (lying in [0, 1]) into the training and update of the fuzzy classifiers, which measure the importance levels of the various features and can be smoothly updated with new incoming samples. In some cases, when the weights become (approximately) 0, an automatic switching off of some features and therefore a (soft) dimension reduction is achieved. The approaches for incrementally updating the feature weights are based on a leave-one-feature-out and on a feature-wise separability criterion. We will describe the integration concept of the feature weights in evolving fuzzy classifiers using single and multi-model architecture. The whole approach will be evaluated based on high-dimensional on-line real-world classification scenarios.