A review of improved extreme learning machine methods for data stream classification

Classification is a hotspot in data stream mining and has gained increasing interest from various research fields. Compared with traditional data stream classification methods, Extreme Learning Machine (ELM) has attracted much attention because of its efficiency and simplicity, which inspired the development of many improved ELM algorithms that have been proposed in the past few years. This paper mainly reviews the current state of ELM used to classify data streams and its variants. First, we introduce the principles of ELM and the existing problems of data stream classification. Then we provide an overview of various improvements made to ELM, which further improves its stability, accuracy and generalization ability and present the practical applications of ELM used in data stream classification. Finally, the paper highlights the existing problems of ELM used for data stream mining and development prospects of ELM in the future.