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Interval-based non-dimensionalization method (IBNM) and its application

  • Mathematical methods in data science
  • Published:
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Abstract

In the face of interval sensitive data, aiming at the disadvantages of rationality and adaptability of linear dimensionless method, as well as the complexity of constructing polyline and curve dimensionless method, this paper proposes an Interval-based Non-dimensionalization Method (IBNM). Assuming that the data can be divided into n levels within its domain, IBNM divides n intervals based on these n grades. N + 1 connection points were set by taking the critical points between the intervals as abscissa and the sequence values corresponding to the n grades of the critical points as ordinate. Then, the dimensionless transformation function IBNM is constructed by connecting adjacent connection points according to fuzzy mathematics theory. If the connection mode of IBNM is simple piecewise linear function, then called it polyline IBNM. Accordingly, if the connection mode adopts exponential function, logarithmic function and other curve functions, it is called curve IBNM. IBNM is scientific, reasonable, simple and practical. This paper takes PM2.5 air quality grade prediction as an example and constructs four kinds of air quality grade prediction models. A variety of traditional dimensionless methods, polyline IBNM and curve IBNM were used to process the data, respectively, and were applied to these prediction models. The results show that the effect of polyline IBNM and curve IBNM is better than that of traditional non-dimensionalization methods.

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Data availability

The datasets generated during and analyzed during the current study are available through http://www.stateair.net/web/post/1/3.html, http://www.stateair.net/web/post/1/3.html.

Notes

  1. http://www.stateair.net/web/post/1/3.html

  2. http://www.stateair.net/web/post/1/3.html

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Funding

This work was supported by National Natural Science Foundation of China (Grant No. 61772146), the Colleges Innovation Project of Guangdong (Grant No. 2016KTSCX036), Guangzhou program of Philosophy and Science Development for 13rd 5-Year Planning (Grant No. 2018GZGJ40), Guangdong Key Lab of Ocean Remote Sensing (LORS). (2017B030301005, GDJ20154400004), and GuangDong University of Foreign Studies (17ss13).

Author information

Author notes

  1. Tianjiao Xu and Shihong Chen have contributed equally to this work.

Authors and Affiliations

  1. Natural Language Processing and Portuguese-Chinese Machine Translation Laboratory (NLP2CT), Faculty of Science and Technology, University of Macau, Taipa, 999078, Macau, China

    Tianjiao Xu

  2. Laboratory of Language Engineering and Computing, School of Information Science and Technology, Guangdong University of Foreign Studies, Guangzhou, 510000, China

    Shihong Chen

  3. College of mechanical and electrical engineering, Guangdong Engineering Polytechnic, Guangzhou, 510000, China

    Yan Ye

  4. School of communication, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Baiqi Li

  5. School of Economics and Trade, Guangdong University of Foreign Studies, Guangzhou, 510000, China

    Huaping Guan

Authors
  1. Tianjiao Xu
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  2. Shihong Chen
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  3. Yan Ye
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  4. Baiqi Li
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  5. Huaping Guan
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Contributions

Experiment, investigation, formal analysis, writing—review and editing, TX; conceptualization, methodology, resources, writing—original draft preparation, SC; data curation, translation, BL; project administration, YY; validation, supervision, HG; funding acquisition, SC and HG All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Huaping Guan.

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The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.

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This study does not contain any studies with human participants or animals performed by any of the authors.

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Xu, T., Chen, S., Ye, Y. et al. Interval-based non-dimensionalization method (IBNM) and its application. Soft Comput 26, 11425–11434 (2022). https://doi.org/10.1007/s00500-022-07474-1

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  • DOI: https://doi.org/10.1007/s00500-022-07474-1

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