title: A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM.
abstract: This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively.
authors.affiliation: Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Li
authors.affiliation: Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.
authors.forename: Quanxin
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.
authors.forename: Yalei
authors.initials: Y
authors.lastname: Wu
authors.affiliation: China Academy of Space Technology, Beijing, China.
authors.forename: Shimin
authors.initials: S
authors.lastname: Song
authors.affiliation: China Academy of Space Technology, Beijing, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Sun
authors.affiliation: China Academy of Space Technology, Beijing, China.
authors.forename: Tengchong
authors.initials: T
authors.lastname: Liu
authors.affiliation: Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.affiliation: Institute of Artificial Intelligence and Robotics, Xian Jiaotong University, Xian Shanxi Province, China.
authors.forename: Shaoyi
authors.initials: S
authors.lastname: Du
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D004560:Electricity; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D018531:Spacecraft; D060388:Support Vector Machine