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Dynamically evolving fuzzy classifier for real-time classification of data streams

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In this paper, a novel evolving fuzzy rule-based classifier is presented. The proposed classifier addresses the three fundamental issues of data stream learning, viz., computational efficiency in terms of processing time and memory requirements, adaptive to changes, and robustness to noise. Though, there are several online classifiers available, most of them do not take into account all the three issues simultaneously. The newly proposed classifier is inherently adaptive and can attend to any minute changes as it learns the rules in online manner by considering each incoming example. However, it should be emphasized that it can easily distinguish noise from new concepts and automatically handles noise. The performance of the classifier is evaluated using real-life data with evolving characteristic and compared with state-of-the-art adaptive classifiers. The experimental results show that the classifier attains a simple model in terms of number of rules. Further, the memory requirements and processing time per sample does not increase linearly with the progress of the stream. Thus, the classifier is capable of performing both prediction and model update in real-time in a streaming environment.