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The attribute reduction method modeling and evaluation based on flight parameter data

  • S.I. : Brain- Inspired computing and Machine learning for Brain Health
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Abstract

This article focuses on the flight parameter data attribute reduction modelling and evaluation problem. From a structural perspective, flight parameter data analysis has two mainly problems, dimensions and measures. To handle the problems, the attribute of the flight parameter should be reduced. The processed parameter data can be modeled to analyze the flight safety problems. This paper proposes an attribute reduction method with the flight parameter data of the landing phase, which is period the security incidents occurred most frequently. The study applies the neighbourhood rough set to attribute reduction. The proposed attribute reduction method was evaluated and compared with the attribute reduction of factor analysis. The result suggests that the proposed method has higher prediction accuracy.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 71271009, 71501007 and 71672006), the Aviation Science Foundation of China (2017ZG51081), the Technical Research Foundation (JSZL2016601A004) and the Graduate Student Education and Development Foundation of Beihang University.

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Authors and Affiliations

  1. School of Reliability and System Engineering, Beihang University, Beijing, China

    Wenbing Chang, Zhenzhong Xu, Xingxing Xu & Shenghan Zhou

  2. Center for Industrial Production, Aalborg University, Aalborg, Denmark

    Yang Cheng

Authors
  1. Wenbing Chang
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  2. Zhenzhong Xu
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  3. Xingxing Xu
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  4. Shenghan Zhou
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  5. Yang Cheng
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Correspondence to Shenghan Zhou.

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Chang, W., Xu, Z., Xu, X. et al. The attribute reduction method modeling and evaluation based on flight parameter data. Neural Comput & Applic 32, 51–60 (2020). https://doi.org/10.1007/s00521-018-3742-4

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  • DOI: https://doi.org/10.1007/s00521-018-3742-4

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