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An experimental study on symbolic extreme learning machine

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Abstract

With the advent of big data era, the volume and complexity of data have increased exponentially and the type of data has also been increased largely. Among all different types of data, symbolic data plays an important role in the study on machine learning model. It has been proved that feed-forward neural network (FNN) has a good ability to deal with numeric data but relatively clumsy with symbolic data. In this paper, a special type of FNN called Extreme Learning Machine (ELM) is discussed for handling symbolic data. Experimental results demonstrate that, unlike traditional back propagation based FNN, ELM has a better performance in comparison with C4.5 which is generally acknowledged as one of the best algorithms in handling symbolic data classification problems. In this performance comparison, some key evaluation criteria such as generalization ability, time complexity, the effect of training sample size and noise-resistance ability are taken into account.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Numbers 61772344 and 61732011), in part by the Natural Science Foundation of SZU (Grant Numbers 827-000140, 827-000230, and 2017060), in part by Guangdong Province 2014GKXM054, and in part by Hebei Educational Committee Foundation (QN2018226).

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Authors and Affiliations

  1. College of Electrical and Electronic Engineering, Shijiazhuang Tiedao University, Shijiazhuang, China

    Jinga Liu, XiaoYun Sun & Kai Tao

  2. Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Guangdong, China

    Muhammed J. A. Patwary

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  1. Jinga Liu
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  2. Muhammed J. A. Patwary
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  3. XiaoYun Sun
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  4. Kai Tao
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Correspondence to XiaoYun Sun.

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Liu, J., Patwary, M.J.A., Sun, X. et al. An experimental study on symbolic extreme learning machine. Int. J. Mach. Learn. & Cyber. 10, 787–797 (2019). https://doi.org/10.1007/s13042-018-0872-z

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  • DOI: https://doi.org/10.1007/s13042-018-0872-z

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