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A Novel Extreme Learning Machine-Based Classification Algorithm for Uncertain Data

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Trends and Applications in Knowledge Discovery and Data Mining (PAKDD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10526))

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Abstract

Traditional classification algorithms are widely used on determinate data. However, uncertain data is ubiquitous in many real applications, which poses a great challenge to traditional classification algorithms. Extreme learning machine (ELM) is a traditional and powerful classification algorithm. However, existing ELM-based uncertain data classification algorithms can not deal with data uncertainty well. In this paper, we propose a novel ELM-based uncertain data classification algorithm, called UELM. UELM firstly employs exact probability density function (PDF) instead of expected values or sample points to model uncertain data, thus avoiding the loss of uncertain information (probability distribution information of uncertain data). Furthermore, UELM redesigns the traditional ELM algorithm by modifying the received content of input layer and the activation function of hidden layer, thus making the ELM algorithm more applicable to uncertain data. Extensive experimental results on different datasets show that our proposed UELM algorithm outperforms the baselines in accuracy and efficiency.

This work was supported by 863 project of China (No. 2015AA015403) and NSFC (No. 61632019).

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Authors and Affiliations

  1. School of Software Technology, Dalian University of Technology, Dalian, 116620, China

    Xianchao Zhang, Daoyuan Sun, Han Liu & Wenxin Liang

  2. Shanghai University of Finance and Economics, Shanghai, 200433, China

    Yuangang Li

  3. Goldpac Limited, Zhuhai, 519070, China

    Yuangang Li

Authors
  1. Xianchao Zhang
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  2. Daoyuan Sun
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  3. Yuangang Li
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  4. Han Liu
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  5. Wenxin Liang
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Corresponding author

Correspondence to Wenxin Liang .

Editor information

Editors and Affiliations

  1. Seoul National University, Seoul, Korea (Republic of)

    U Kang

  2. School of Information Systems, Singapore Management University, Singapore, Singapore

    Ee-Peng Lim

  3. Chinese University of Hong Kong, Hong Kong, China

    Jeffrey Xu Yu

  4. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Zhang, X., Sun, D., Li, Y., Liu, H., Liang, W. (2017). A Novel Extreme Learning Machine-Based Classification Algorithm for Uncertain Data. In: Kang, U., Lim, EP., Yu, J., Moon, YS. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10526. Springer, Cham. https://doi.org/10.1007/978-3-319-67274-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-67274-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67273-1

  • Online ISBN: 978-3-319-67274-8

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