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An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias

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Abstract

Since extreme learning machine (ELM) was proposed, hundreds of studies have been conducted on this subject in various areas, from theoretical researches to practical applications. However, there are very few papers in the literature to reveal the reasons why in ELM classification the class with the highest output value is being chosen as the predicted class for a given input. In order to give a clear insight into this question, this paper analyzes the rationality of ELM reasoning from the perspective of its inductive bias. The analysis results show that the choice of highest output in ELM is reasonable for both binary and multiclass classification problems. In addition, to deal with multiclass problems ELM uses the well-known one-against-all strategy, in which unclassifiable regions may exist. This paper also gives a clear explanation on how ELM resolves the unclassifiable regions, through both analysis and experiments.

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Acknowledgments

This study was funded by the University of Macau Research Grant (Grant Nos: MYRG2014-00178-FST, MYRG2014-00083-FST and MYRG075(Y1-L2)-FST13-VCM) and the Science and Technology Development Fund of Macau (Grant No: FDCT/050/2015/A). Pak Kin Wong and Chi Man Vong have received research grants from the University of Macau and the Science and Technology Development Fund of Macau S.A.R.

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Authors and Affiliations

  1. Department of Electromechanical Engineering, University of Macau, Macau, China

    Pak Kin Wong, Xiang Hui Gao & Ka In Wong

  2. Department of Computer and Information Science, University of Macau, Macau, China

    Chi Man Vong

Authors
  1. Pak Kin Wong
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  2. Xiang Hui Gao
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  3. Ka In Wong
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  4. Chi Man Vong
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Correspondence to Pak Kin Wong.

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Pak Kin Wong, Xiang Hui Gao, Ka In Wong and Chi Man Vong declare that they have no conflict of interest.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Wong, P.K., Gao, X.H., Wong, K.I. et al. An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias. Cogn Comput 8, 746–756 (2016). https://doi.org/10.1007/s12559-016-9414-8

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  • DOI: https://doi.org/10.1007/s12559-016-9414-8

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