A New Classifier for Handling Concept Drifting Data Stream

Concept drifting stream data mining have recently garnered a great deal of attention for Machine Learning Researcher. The major challenges in stream data mining are focused on speed of data arrival, changes in data distribution in certain time, storage capability that uses less memory, and adapting changes in small amount of time. In this paper, a new Classifier based on hybrid approach is proposed that handle concept drifting stream data. The proposed classifier is used Naives Bayes as base learner for classification of concept drifting stream data where as concept drift is detected and handled by using drift detection method.