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Trends and Applications in Knowledge Discovery and Data Mining pp 189–197Cite as

Model Selection of Symbolic Regression to Improve the Accuracy of PM2.5 Concentration Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9441))

Abstract

As one of the main components of haze, topics with respect to PM2.5 are coming into people’s sight recently in China. In this paper, we try to predict PM2.5 concentrations in Dalian, China via symbolic regression (SR) based on genetic programming (GP). During predicting, the key problem is how to select accurate models by proper interestingness measures. In addition to the commonly used measures, such as R-squared value, mean squared error, number of parameters, etc., we also study the effectiveness of a set of potentially useful measures, such as AIC, BIC, HQC, AICc and EDC. Besides, a new interestingness measure, namely Interestingness Elasticity (IE), is proposed in this paper. From the experimental results, we find that the new measure gains the best performance on selecting candidate models and shows promising extrapolative capability.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (71001016).

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Authors and Affiliations

  1. School of Management Science and Engineering, Dalian University of Technology, Dalian, China

    Guangfei Yang & Jian Huang

Authors
  1. Guangfei Yang
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  2. Jian Huang
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Corresponding author

Correspondence to Guangfei Yang .

Editor information

Editors and Affiliations

  1. Institute of Infocomm Research, Singapore, Singapore

    Xiao-Li Li

  2. Ho Chi Minh City University of Tech, Ho Chi Minh City, Vietnam

    Tru Cao

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

    Ee-Peng Lim

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Science & Technology, Japan Advanced Institute of, Nomi-shi, Ishikawa, Japan

    Tu-Bao Ho

  6. The University of Hong Kong, Hong Kong, China

    David Cheung

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Yang, G., Huang, J. (2015). Model Selection of Symbolic Regression to Improve the Accuracy of PM2.5 Concentration Prediction. In: Li, XL., Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D. (eds) Trends and Applications in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science(), vol 9441. Springer, Cham. https://doi.org/10.1007/978-3-319-25660-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-25660-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25659-7

  • Online ISBN: 978-3-319-25660-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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