An experimental study on symbolic extreme learning machine View Full Text


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Article Info

DATE

2019-04

AUTHORS

Jinga Liu, Muhammed J. A. Patwary, XiaoYun Sun, Kai Tao

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. More... »

PAGES

787-797

References to SciGraph publications

  • 2014-09. ELM ∗ : distributed extreme learning machine with MapReduce in WORLD WIDE WEB
  • 2008-01. Top 10 algorithms in data mining in KNOWLEDGE AND INFORMATION SYSTEMS
  • 2018-01. Recent advances of statistics in computational intelligence (RASCI) in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 1986-03. Induction of decision trees in MACHINE LEARNING
  • 1991-03. Symbolic and neural learning algorithms: An experimental comparison in MACHINE LEARNING
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    http://scigraph.springernature.com/pub.10.1007/s13042-018-0872-z

    DOI

    http://dx.doi.org/10.1007/s13042-018-0872-z

    DIMENSIONS

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