Concept drift detection and adaption framework using optimized deep learning and adaptive sliding window approach

Abstract

AbstractConcept drift in online streaming data is a common issue due to dynamic smart systems, which results in system failure or performance degradation. Though there are several traditional approaches for handling the streaming data, they failed to handle the concept drift imposing the need for developing an adaptable approach to managing the dynamic IoT streaming data. Therefore, in this research, a new method is proposed for handling the concept drift issues in online data streaming. This research develops the dynamic streaming data analytic framework based on the optimized Deep CNN and optimized adaptive and sliding window (OASW) approach that effectively addresses both memory and time constraints. An optimized Deep CNN classifier is employed as a base classifier for offline learning, which is developed through hybridizing the proposed Desale's aggressive hunt optimization (AHO) algorithm with a Deep CNN classifier for tuning the optimal parameters of the classifier. An optimized adaptive and sliding window is utilized in this research to adapt the pattern changes in the data streams, which effectively handles the concept drift. The experimental analysis reveals that the proposed methods outperform the conventional methods considered for the analysis in terms of specificity, sensitivity, accuracy, F1 score, and the precision score of 96.65%, 97.77%, 98.63%, 98.1487%, and 98.4469%, respectively.