Most preferable combination of explicit drift detection approaches with different classifiers for mining concept drifting data streams

Abstract

Sensors in the real-world applications are the major sources of big data streams with varying underlying data distribution. Continuously generated time varying data streams are commonly referred as concept drifting data streams. Many concept drifting data mining algorithms explicitly utilise the drift detection algorithms for ensuring the forgetting of out-dated concepts and learn new concepts upon occurrence of drifts. In concept drifting data streams, the accuracy of the learner depends on the accuracy of the drift detection algorithm and its promptness towards drifts detection. For maintaining the consistent high accuracy in the classification of concept drifting data streams, it is very important to understand the preferable combinations of drift detection algorithms with the classification algorithms. In order to explore such preferable combinations, this work presents an empirical evaluation of some popular drift detection methods with some state-of-art classification algorithms on some standard benchmark datasets of real world.