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Accelerating ELM Training over Data Streams

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Proceedings of ELM 2018 (ELM 2018)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 11))

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Abstract

As a machine learning method, extreme learning machine (ELM) has the characteristics of fast learning speed and high accuracy. With the explosive growth of data volume, running machine learning algorithms on distributed computing platforms is an unstoppable trend. Apache Flink is an open-source stream-based distributed platform for massive data processing with good scalability, high throughput, and fault-tolerant ability. In this paper, we first research the characteristics of ELM and distributed computing platforms, then propose a distributed ELM framework (FL-ELM) which is based on Flink. Then we evaluate this framework with synthetic data on a 5-node distributed cluster. In summary, the advantages of the proposed framework is highlighted as follows: (1) The training speed of FL-ELM is always faster than that in Spark; (2) The scalability of FL-ELM behave better than that in Spark.

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References

  1. Apache flink. http://flink.apache.org/

  2. Apache hadoop. http://hadoop.apache.org/

  3. Apache spark. http://spark.apache.org/

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Acknowledgments

This research was partially supported by the National Key Research and Development Program of China under Grant No. 2018YFB1004402; and the National Natural Science Foundation of China under Grant No. 61872072, U1401256, 67132003.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Hangxu Ji & Gang Wu

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Guoren Wang

Authors
  1. Hangxu Ji
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  2. Gang Wu
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  3. Guoren Wang
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Corresponding author

Correspondence to Hangxu Ji .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Xiasha, Hangzhou, China

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Taipa, Macao

    Chi Man Vong

  3. Nokia Bell Labs, Cybersecurity Research, Espoo, Finland

    Yoan Miche

  4. Department of Information and Logistics Technology, University of Houston, Houston, TX, USA

    Amaury Lendasse

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Ji, H., Wu, G., Wang, G. (2020). Accelerating ELM Training over Data Streams. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_20

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