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

被引：21

作者：

Li, Li ^{[1
]}

Sun, Ruizhi ^{[1
,2
]}

Cai, Saihua ^{[1
]}

Zhao, Kaiyi ^{[1
]}

Zhang, Qianqian ^{[1
]}

机构：

[1] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China

[2] Minist Agr, Sci Res Base Integrated Technol Precis Agr Anim H, Beijing 100083, Peoples R China

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2019年 / 78卷 / 23期

关键词：

Data streams; Classification; Improved extreme leaning machine; Concept drifts; Imbalanced data streams; Uncertain data streams; INTRUSION DETECTION SYSTEM; DRIFTING DATA STREAMS; CLASS IMBALANCE; ONLINE; ENSEMBLE; ALGORITHM; UNCERTAIN; NETWORK; SCHEME; MODEL;

D O I：

10.1007/s11042-019-7543-2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：33375 / 33400

页数：26

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