Fig. 2

Using variance to detect concept drift for different data sets. The x-axis represents time and the y-axis represents the model’s predictive variance for the stream. Initially, when stream consists of samples generated from known modes, variance is low. Later, when samples from an unrecognized mode appear, variance drastically shoots up. For the first two data sets, noisy instances in the initial part get assigned a large variance, sporadically. The variance is well-behaved for the third data set. The optimal values of hyper-parameters, \(n_s\) and \(\sigma _t\), were set to (1000, 0.7), (412, 1.2), (855, 0.5), for the three data sets