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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: 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.

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.


Classification accuracy use different algorithm.Naive-Bayes algorithm, the KNN algorithm, one-to-one SVM, one-to-the-other SVM, one-to-one WPSVM and one-to-the-other WPSVM are used in the on-line system. It shows the accuracy of test data identification.
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pone.0140395.g013: Classification accuracy use different algorithm.Naive-Bayes algorithm, the KNN algorithm, one-to-one SVM, one-to-the-other SVM, one-to-one WPSVM and one-to-the-other WPSVM are used in the on-line system. It shows the accuracy of test data identification.

Mentions: The feature extraction simulation experiment is conducted similar to the PCA simulation experiments. The identification of accuracy for test data in the on-line system using different methods are given (Fig 13), which include the naive-Bayes algorithm, the KNN algorithm, one-to-one SVM, one-to-the-other SVM, one-to-one WPSVM and one-to-the-other WPSVM. The conclusion is that the WPSVM algorithm that we propose has better accuracy than the classical algorithms. When the sample number is 100, the classification performance is more obvious.


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)

Classification accuracy use different algorithm.Naive-Bayes algorithm, the KNN algorithm, one-to-one SVM, one-to-the-other SVM, one-to-one WPSVM and one-to-the-other WPSVM are used in the on-line system. It shows the accuracy of test data identification.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0140395.g013: Classification accuracy use different algorithm.Naive-Bayes algorithm, the KNN algorithm, one-to-one SVM, one-to-the-other SVM, one-to-one WPSVM and one-to-the-other WPSVM are used in the on-line system. It shows the accuracy of test data identification.
Mentions: The feature extraction simulation experiment is conducted similar to the PCA simulation experiments. The identification of accuracy for test data in the on-line system using different methods are given (Fig 13), which include the naive-Bayes algorithm, the KNN algorithm, one-to-one SVM, one-to-the-other SVM, one-to-one WPSVM and one-to-the-other WPSVM. The conclusion is that the WPSVM algorithm that we propose has better accuracy than the classical algorithms. When the sample number is 100, the classification performance is more obvious.

Bottom Line: 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.

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.