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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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References

  1. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993

    Article  Google Scholar 

  2. Kwok TY, Yeung DY (1997) Constructive algorithm for structure learning feedforward neural networks for regression problems. IEEE Trans Neural Netw 8(3):630–650

    Article  Google Scholar 

  3. Huang GB, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892

    Article  Google Scholar 

  4. Liang NY, Huang GB, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411–1423

    Article  Google Scholar 

  5. Jamli MR (2015) Incorporating feedforward neural network within finite element analysis for L-bending spring back prediction. Expert Syst Appl 42(5):2604–2614

    Article  Google Scholar 

  6. Quinlan JR (1984) Induction of decision trees. Mach Learn 1:84–101

    Google Scholar 

  7. Wu X, Kumar V, Quinlan JR (2007) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37

    Article  Google Scholar 

  8. Quinlan JR (2014) C4.5: programs for machine learning. Morgan Kaufmann Publishers, Burlington

    Google Scholar 

  9. Jia C, Chen G (2016) Application of C4.5 algorithm based on rough set. J Chin Agric Mech 37:149–152

    Google Scholar 

  10. Quinlan JR (1987) Simplifying decision trees. Int J Man Mach Stud 27(3):221–234

    Article  Google Scholar 

  11. Quinlan JR (1989) Unknown attribute values in induction. Proceedings of the Sixth International Workshop on Machine Learning, Cornell University, Ithaca, New York, Morgan Kaufmann, Elsevier Inc., 26–27 June 1989

  12. Shavlik JW, Mooney RJ, Towell GG (1991) Symbolic and neural learning algorithms: an experimental comparison. Mach Learn 6(2):111–143

    MATH  Google Scholar 

  13. Funahashi KI, Nakamura Y (1992) Neural networks, approximation theory, and dynamical systems (structure and bifurcation of dynamical systems). Rims Kokyuroku 804:18–37

    MATH  Google Scholar 

  14. Schmidt WF, Kraaijveld MA, Duin RPW (1992) Feedforward neural networks with random weights, vol 2. In: Proc. of 11th IAPR international conference on pattern recognition. Conference B: pattern recognition methodology and systems, IEEE, pp 1–4

  15. Pao YH, Takefji Y (1992) Functional-link net computing. IEEE Comput J 25(5):76–79

    Article  Google Scholar 

  16. Pao YH, Park GH, Sobajic DJ (1994) Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6:163–180

    Article  Google Scholar 

  17. Igelnik B, Pao YH (1995) Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans Neural Netw 6(6):1320–1329

    Article  Google Scholar 

  18. Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355

    MathSciNet  MATH  Google Scholar 

  19. Huang GB, Zhu QY, Siew CK (2004) ELM: a new learning scheme of feedforward neural networks. In: Proceedings of the international joint conference on neural networks (IJCNN2004), Budapest, pp 25–29

    Google Scholar 

  20. Xin JC, Wang ZQ (2015) Elastic ELM for big data classification. Neurocomputing 149:464–471

    Article  Google Scholar 

  21. Xin JC, Wang ZQ (2014) ELM*:distributed ELM with MapReduce. World Wide Web 17:1189–1204

    Article  Google Scholar 

  22. Zhao SX, Wang XZ (2014) ELM for interval-valued data. Machine learning and cybernetics. Springer, Berlin, pp 388–399

    Book  Google Scholar 

  23. Huang GB et al (2005) On-line sequential extreme learning machine. In: The IASTED international conference on computational intelligence (CI 2005), Calgary, 4–6 July

    Google Scholar 

  24. Huang GB et al (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892

    Article  Google Scholar 

  25. Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062

    Article  Google Scholar 

  26. Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468

    Article  Google Scholar 

  27. Miche Y et al (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162

    Article  Google Scholar 

  28. Dou T, Zhou X (2015) The fast computation methods for extreme learning machine. In: 2015 2nd international conference on intelligent computing and cognitive informatics (ICICCI 2015). Atlantis Press

  29. Xi-Zhao W et al (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102:3–9

    Article  Google Scholar 

  30. Khosrowabadi R et al (2014) ERNN: a biologically inspired feedforward neural network to discriminate emotion from EEG signal. IEEE Trans Neural Netw Learn Syst 25(3):609–620

    Article  Google Scholar 

  31. De Luca A, Termini S (1972) A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Inf Control 20(4):301–312

    Article  MathSciNet  MATH  Google Scholar 

  32. Patwary MJA, Lui JNK, Dai H (2018) Recent advances of statistics in computational intelligence (RASCI). Int J Mach Learn Cybern 9(1):1–3

    Article  Google Scholar 

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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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