  Increasingly, Internet of Things (IoT) domains, such as sensor networks,
smart cities, and social networks, generate vast amounts of data. Such data are
not only unbounded and rapidly evolving. Rather, the content thereof
dynamically evolves over time, often in unforeseen ways. These variations are
due to so-called concept drifts, caused by changes in the underlying data
generation mechanisms. In a classification setting, concept drift causes the
previously learned models to become inaccurate, unsafe and even unusable.
Accordingly, concept drifts need to be detected, and handled, as soon as
possible. In medical applications and emergency response settings, for example,
change in behaviours should be detected in near real-time, to avoid potential
loss of life. To this end, we introduce the McDiarmid Drift Detection Method
(MDDM), which utilizes McDiarmid's inequality in order to detect concept drift.
The MDDM approach proceeds by sliding a window over prediction results, and
associate window entries with weights. Higher weights are assigned to the most
recent entries, in order to emphasize their importance. As instances are
processed, the detection algorithm compares a weighted mean of elements inside
the sliding window with the maximum weighted mean observed so far. A
significant difference between the two weighted means, upper-bounded by the
McDiarmid inequality, implies a concept drift. Our extensive experimentation
against synthetic and real-world data streams show that our novel method
outperforms the state-of-the-art. Specifically, MDDM yields shorter detection
delays as well as lower false negative rates, while maintaining high
classification accuracies.
