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Link to original content: https://doi.org/10.1007/s12559-016-9409-5
Self-adaptive Extreme Learning Machine Optimized by Rough Set Theory and Affinity Propagation Clustering | Cognitive Computation Skip to main content
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Self-adaptive Extreme Learning Machine Optimized by Rough Set Theory and Affinity Propagation Clustering

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Abstract

Recently, a simple and efficient learning algorithm for single hidden layer feedforward networks (SLFNs) called extreme learning machine (ELM) has been developed by G.-B. Huang et al. One key strength of ELM algorithm is that there is only one parameter, the number of hidden nodes, to be determined while it has the significantly low computational time required for training new classifiers and good generalization performance. However, there is no effective method for finding the proper and universal number of hidden nodes. In order to address this problem, we propose a self-adaptive extreme learning machine (SELM) algorithm. SELM algorithm determines self-adaptively the number of hidden nodes and constructs Gaussian function as activation functions of hidden nodes. And in this algorithm, rough set theory acts as the pre-treatment cell to eliminate the redundant attributes of data sets. Then, affinity propagation clustering (AP Clustering) is used to self-adaptively determine the number of hidden nodes, while the centers and widths of AP clustering are utilized to construct a Gaussian function in the hidden layer of SLFNs. Empirical study of SELM algorithm on several commonly used classification benchmark problems shows that the proposed algorithm can find the proper number of hidden nodes and construct compact network classifiers, comparing with traditional ELM algorithm.

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Acknowledgments

This work is supported by the Fundamental Research Funds for the Central Universities (No. 2015XKMS088).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Li Xu, Shifei Ding, Xinzheng Xu & Nan Zhang

  2. Jiangsu Key Laboratory of Mine Mechanical and Electrical Equipment, China University of Mining & Technology, Xuzhou, 221116, China

    Li Xu, Shifei Ding, Xinzheng Xu & Nan Zhang

Authors
  1. Li Xu
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  2. Shifei Ding
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  3. Xinzheng Xu
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Correspondence to Shifei Ding.

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Li Xu, Shifei Ding, Xinzheng Xu and Nan Zhang 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 or animal subjects performed by the any of the authors.

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Xu, L., Ding, S., Xu, X. et al. Self-adaptive Extreme Learning Machine Optimized by Rough Set Theory and Affinity Propagation Clustering. Cogn Comput 8, 720–728 (2016). https://doi.org/10.1007/s12559-016-9409-5

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  • DOI: https://doi.org/10.1007/s12559-016-9409-5

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