A Self-adaptive Fuzzy Network for Prediction in Non-stationary Environments

Prediction in non-stationary environments, where data streams are ever-changing at very high speeds, has become more and more important in real-world applications. The uncertainty in data streams caused by changes in data distribution is described as concept drift. The appearance of concept drift in a data stream results in inconsistencies between the existing data and incoming data. Such inconsistencies pose a great challenge to conventional machine learning methods, given they are built on the assumption of independent and identically distributed data and cannot adapt to unpredictable changes in knowledge patterns. To solve such data stream uncertainty problem, this paper presents a window-based self-adaptive fuzzy network called adaptive fuzzy network (AFN), which can continuously modify the network through identifying new knowledge from the previous data samples. Three components are embedded in ANF: a drift detection module to identify whether the current window of data samples presents different pattern from the previous; a drift adaption module to retain useful knowledge in previous samples; and a fuzzy inference system, which integrates the detection and adaption modules for prediction. ANF has been evaluated through a set of experiments on non-stationary data streams. The experimental results show a good effectiveness of our method.