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Extreme Learning Machine Based Diagnosis Models for Erythemato-Squamous Diseases

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Health Information Science (HIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11148))

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Abstract

Extreme learning machine based features selection algorithms are proposed in this paper for diagnosing erythemato-squamous diseases. The algorithms adopt the traditional ELM (extreme learning machine), EM-ELM (the error minimum extreme learning machine) and K-ELM (kernel extreme learning machine), respectively, to evaluate the power of the detected feature subset. The improved F-score and SFS (sequential forward search) strategy are combined to detect feature subsets. To detect a much more accurate diagnosis model for erythemato-squamous diseases, an ensemble diagnosis model is constructed by combining three models (classifiers) built on three feature subsets detected by proposed feature selection algorithms respectively. 5-fold cross validation experiments are conducted to test the performance of each feature selection algorithm, and the ensemble model. Experimental results demonstrate that the ensemble model has got the best accuracy. Its highest and average classification accuracy in 5-fold cross validation experiments are 100% and 98.31%, respectively.

Supported by NSFC under Grant No. 61673251 & by Fundamental Research Funds for Central Universities under Grant No. GK201701006 & by the Innovation Funds of Graduate Programs at Shaanxi Normal University under Grant No. 2015CXS028 and 2016CSY009.

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Author information

Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, People’s Republic of China

    Juanying Xie & Xinyuan Ji

  2. College of Life Science, Shaanxi Normal University, Xi’an, 710119, People’s Republic of China

    Mingzhao Wang

Authors
  1. Juanying Xie
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  2. Xinyuan Ji
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  3. Mingzhao Wang
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Corresponding author

Correspondence to Juanying Xie .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  2. James Cook University, Cairns, QLD, Australia

    Ickjai Lee

  3. Vrije University of Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Footscray, VIC, Australia

    Hua Wang

  6. James Cook University, Cairns, QLD, Australia

    Wei Xiang

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Xie, J., Ji, X., Wang, M. (2018). Extreme Learning Machine Based Diagnosis Models for Erythemato-Squamous Diseases. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-01078-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01077-5

  • Online ISBN: 978-3-030-01078-2

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