HWEFIS: A Hybrid Weighted Evolving Fuzzy Inference System for Nonstationary Data Streams

Abstract

For the problem of concept drift of nonstationary data streams, most evolving fuzzy inference systems (EFISs) still encounter problems. First, a single EFIS has difficulty quickly adjusting its own structure and parameters to adapt itself in an environment with obvious dynamic changes (such as sudden drift). Second, most ensemble EFISs adjust their weights according to errors, which is prone to the risk of model undertraining and repeated training. In this article, a new ensemble EFIS, referred to as a hybrid weighted evolving fuzzy inference system (HWEFIS), is proposed. The HWEFIS uses a detection method based on the edge heterogeneous distance (EHD) to mine similarity information between data distributions after data chunks arrive and uses Dempster–Shafer (DS) evidence theory to combine similarity and error information to generate hybrid weights. In addition, a forgetting factor and penalty mechanism are introduced into each base learner, which increases the ability of the base learner to address nonstationary problems. Experiments are carried out on synthetic datasets and real-world datasets. The experimental results show that the HWEFIS can achieve better performance in nonstationary data streams with complex drift, effectively suppresses the influence of concept drift, and is insensitive to the size of the data chunks.