The volume of IoT data is rapidly increasing due to the development of the technology of information and communication. This data comes mostly in the form of streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. Traditional one shot memory-based learning methods trained offline from a static historic data cannot cope with evolving data streams. This is because firstly, it is not feasible to store all incoming data over time and secondly the generated models become quickly obsolete due to data distribution changes, also known as “concept drift.” The basic assumption of offline learning is that data is generated by a stationary process and the learning models are consistent with future data. However, in multiple applications like IoT, web mining, social networks, network monitoring, sensor networks, telecommunications, financial forecasting, etc., data samples arrive continuously as unlimited streams often at high speed. Moreover, the phenomena generating these data streams may evolve over time. In this case, the environment in which the system or the phenomenon generated the data is considered to be dynamic, evolving, or non-stationary.