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Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

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Abstract

Many time series problems such as air pollution index forecast require online sequential learning rather than batch learning. One of the major obstacles for air pollution index forecast is the data imbalance problem so that forecast model biases to the majority class. This paper proposes a new method called meta-cognitive online sequential extreme learning machine (MCOS-ELM) that aims to alleviate data imbalance problem and sequential learning at the same time. Under a real application of air pollution index forecast, the proposed MCOS-ELM was compared with retrained ELM and online sequential extreme learning machine in terms of accuracy and computational time. Experimental results show that MCOS-ELM has the highest efficiency and best accuracy for predicting the minority class (i.e., the most important but with fewest training samples) of air pollution level.

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Acknowledgments

The research is supported by the University of Macau Research Grant, Grant No. MYRG141(Y2-L2)-FST11-IWF, MYRG075(Y2-L2)-FST12-VCM.

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Authors and Affiliations

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

    Chi-Man Vong & Chi-Chong Chiu

  2. Faculty of Science and Technology, University of Macau, Macau, China

    Weng-Fai Ip

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

    Pak-Kin Wong

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  1. Chi-Man Vong
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Correspondence to Chi-Man Vong.

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Vong, CM., Ip, WF., Chiu, CC. et al. Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine. Cogn Comput 7, 381–391 (2015). https://doi.org/10.1007/s12559-014-9301-0

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  • DOI: https://doi.org/10.1007/s12559-014-9301-0

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