Fog computing emerged as a contemporary strategy to process big streaming data efficiently. It is designed as a distributed computing platform for supporting the data analytics for Internet of Things (IoT) applications that pushes the data analytics from Cloud server to the far edge of a sensor network. As the name suggests, ubiquitous data which is collected from the sensors are processed locally rather than on the central servers. Fog computing helps avoid performance bottleneck at the center point and relieves raw data from overwhelming towards the center of the network. However, suitable data analysis algorithms such as those of data stream mining that are consist of learning and recognizing patterns from the incoming data streams must be fast and accurate enough for supporting Fog computing. This paper reports about a computer simulation of running data stream mining algorithms in Fog environment. Furthermore, feature selection that is powered by s warm search is used as a pre-processing method for improving the accuracy and speed of local Fog data analytics. Through the experiment, the results reveal which algorithms are the best choice to deliver edge intelligence in Fog computing environment.