@inproceedings{6b86249642534a89844cc3a4c9d568a9,
title = "Spacecraft electrical signal classification method of reliability test based on random forest",
abstract = "The spacecraft electrical signal characteristic data exist a large amount of data, high dimension features, computational complexity degree and low rate of identification problems. This paper proposes the feature extraction method based on wavelet de-noising and the classification method based on random forest (RF) algorithm. Considering the time complexity, the method of wavelet de-noising is used to compress the data and reduce the dimension and then applied to classification. The random forest algorithm has superior performance in dealing with the large amount of data. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in accuracy, computational efficiency, stability in dealing with spacecraft electrical signal data.",
keywords = "Electrical signal classification, RF, Spacecraft fault diagnosis",
author = "Ke Li and Ruicong Ran and Shimin Song and Jun Wang and Lijing Wang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 17th International Conference on Man–Machine–Environment System Engineering, MMESE 2017 ; Conference date: 21-10-2017 Through 23-10-2017",
year = "2018",
doi = "10.1007/978-981-10-6232-2\_53",
language = "英语",
isbn = "9789811062315",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "457--465",
editor = "Shengzhao Long and Dhillon, \{Balbir S\}",
booktitle = "Man–Machine–Environment System Engineering - Proceedings of the 17th International Conference on MMESE",
address = "德国",
}