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A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM.

Li K, Liu Y, Wang Q, Wu Y, Song S, Sun Y, Liu T, Wang J, Li Y, Du S - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.

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.

No MeSH data available.


Membership matrix U.The sample that has high membership can be confirmed directly; other samples which belong to multiple categories with similar membership require further revision by human experts. Compared with k-means algorithm, the FCM algorithm offered an extra matrix U which uses a threshold set by us. Only the elements of the matrix U which are less than the threshold need to be corrected by humans. The matrix U will save much human error correction time in the off-line system.
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pone.0140395.g009: Membership matrix U.The sample that has high membership can be confirmed directly; other samples which belong to multiple categories with similar membership require further revision by human experts. Compared with k-means algorithm, the FCM algorithm offered an extra matrix U which uses a threshold set by us. Only the elements of the matrix U which are less than the threshold need to be corrected by humans. The matrix U will save much human error correction time in the off-line system.

Mentions: An example of a roll event during the spacecraft load test experiment is detailed in Fig 8. The membership matrix U is shown in Fig 9. Compared with k-means algorithm, the FCM algorithm offered an extra matrix U which uses a threshold set by us. Only the elements of the matrix U which are less than the threshold need to be corrected by humans. The matrix U will save much human error correction time in the off-line system.


A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM.

Li K, Liu Y, Wang Q, Wu Y, Song S, Sun Y, Liu T, Wang J, Li Y, Du S - PLoS ONE (2015)

Membership matrix U.The sample that has high membership can be confirmed directly; other samples which belong to multiple categories with similar membership require further revision by human experts. Compared with k-means algorithm, the FCM algorithm offered an extra matrix U which uses a threshold set by us. Only the elements of the matrix U which are less than the threshold need to be corrected by humans. The matrix U will save much human error correction time in the off-line system.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636359&req=5

pone.0140395.g009: Membership matrix U.The sample that has high membership can be confirmed directly; other samples which belong to multiple categories with similar membership require further revision by human experts. Compared with k-means algorithm, the FCM algorithm offered an extra matrix U which uses a threshold set by us. Only the elements of the matrix U which are less than the threshold need to be corrected by humans. The matrix U will save much human error correction time in the off-line system.
Mentions: An example of a roll event during the spacecraft load test experiment is detailed in Fig 8. The membership matrix U is shown in Fig 9. Compared with k-means algorithm, the FCM algorithm offered an extra matrix U which uses a threshold set by us. Only the elements of the matrix U which are less than the threshold need to be corrected by humans. The matrix U will save much human error correction time in the off-line system.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Ergonomics and Environment Control Laboratory, Beihang University, Beijing, China.

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.

No MeSH data available.