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It is based on a self-developing ( e volving) fuzzy-rule-based (FRB) classifier system of T akagi- S ugeno ( eTS ) type. The proposed approach, called eClass ( e volving 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. 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Evolving Fuzzy-Rule-Based Classifiers From Data Streams 论文

Evolving Fuzzy-Rule-Based Classifiers From Data Streams · 相关文章