Abstract
In this era, an emerging field in the data mining is data stream mining. The current research technique of the data stream is classification which mainly focuses on concept drift data. Mining drift data with the single classifier is not sufficient for classifying the data. Because of the high dimensionality and failure to get processed within considerable time and memory, false alarm rate is high, and classification accuracy result is low. In chapter, a proposed genetic-based intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. The proposed method achieves improvement in classification accuracy and perhaps in selecting the least number of features which show the way to simplification of learning task. The experiment shows that the advocated method performs well when compared with existing methods.
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Senthamilarasu, S., Hemalatha, M. (2015). Ensemble Classifier for Concept Drift Data Stream. In: Rajsingh, E., Bhojan, A., Peter, J. (eds) Informatics and Communication Technologies for Societal Development. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1916-3_13
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DOI: https://doi.org/10.1007/978-81-322-1916-3_13
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