Using Inductive Conformal Martingales for addressing concept drift in data stream classification

In this paper, we investigate the use of Inductive Conformal Martingales (ICM) with the histogram betting function for detecting the occurrence of concept drift (CD) in data stream classification. A change in the data distribution will almost surely affect the performance of our classification model resulting in false predictions. Therefore, a reliable and fast detection of the point at which a CD occurs, allows effective retraining of the model to recover accuracy. Our approach is based on ICM with the histogram betting function, which is much more computationally efficient than alternative ICM approaches. To accelerate the process of detecting CD we also modify the ICM and examine different parameters of the histogram betting function. We evaluate the proposed approach on three benchmark datasets, namely STAGGER, SEA and ELEC, presenting different measures of its performance and comparing it with existing methods in the literature.