Testing Concept Drift Detection Technique on Data Stream

Data mutates dynamically, and these transmutations are so diverse that it affects the quality and reliability of the model. Concept Drift is the quandary of such dynamic cognitions and modifications in the data stream which leads to change in the behaviour of the model. The problem of concept drift affects the prognostication quality of the software and thus reduces its precision. In most of the drift detection methods, it is followed that there are given labels for the incipient data sample which however is not practically possible. In this paper, the performance and accuracy of the proposed concept drift detection technique for the classification of streaming data with undefined labels will be tested. Testing is followed with the creation of the centroid classification model by utilizing some training examples with defined labels and test its precision with the test set and then compare the accuracy of the prediction model with and without the proposed concept drift detection technique.