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

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Abstract

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.

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Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Li Li, Ruizhi Sun, Saihua Cai, Kaiyi Zhao & Qianqian Zhang

  2. Scientific research base for Integrated Technologies of Precision Agriculture (animal husbandry), The Ministry of Agriculture, Beijing, 100083, China

    Ruizhi Sun

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  1. Li Li
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  2. Ruizhi Sun
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  3. Saihua Cai
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  4. Kaiyi Zhao
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  5. Qianqian Zhang
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Correspondence to Ruizhi Sun.

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Li, L., Sun, R., Cai, S. et al. A review of improved extreme learning machine methods for data stream classification. Multimed Tools Appl 78, 33375–33400 (2019). https://doi.org/10.1007/s11042-019-7543-2

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  • DOI: https://doi.org/10.1007/s11042-019-7543-2

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