Disposition-Based Concept Drift Detection and Adaptation in Data Stream

The change in data distribution over time (known as concept drift) makes the classification process complex because of the discrepancy between current and incoming data distribution. A plethora of drift detection methods often focus on the early identification of concept drift. Along with the drift, other deformities like noise and blips are also present in the data stream. These deformities may be damaged the underlying learning system by forcing adaptation to false drift. Thereby unnecessary update performs in the learning model that leads to decrease in learner’s accuracy. The existing drift detection methods are not capable of differentiating between actual and false drift. The paper proposes DBDDM, a disposition-based drift detection method, to overcome the issue of false drift. In this paper, we utilize the approximate randomization test to find the frequency of consecutive drift and compare the obtained frequency with the threshold to determine the actual drift. DBDDM compares with the several state-of-the-art methods using synthetic and real-time datasets. It exhibits a maximum increase in accuracy of 24% and 28% with a rise of 2.50 and 1.91 average ranks using Naive Bayes and the Hoeffding tree classifier, respectively.