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Erschienen in:
18.09.2018 | S.I. : Brain- Inspired computing and Machine learning for Brain Health
The attribute reduction method modeling and evaluation based on flight parameter data
Erschienen in: Neural Computing and Applications | Ausgabe 1/2020
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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.