Title:
Ensemble-Classifier-for-Concept-Drift-Data-Stream
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Description: In this era an emerging filed 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. In mining drift data with the single classifier is not sufficient for classifying the data. Because of the high dimensionality and does not get processed within considerable time, memory, false alarm rate is high, classification accuracy result is low. In this paper, proposed a 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 to select the least number of features which show the way to simplification of learning task. The experimental shows that the advocated method performs well when compared with existing methods.
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Ensemble Classifier for Concept Drift Data Stream.doc
